{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "960ceacb-3c3f-484c-95b9-172bd009b1a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a generic code for drawing a state's Home Districts for neutral map estimation\n",
      "In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\n"
     ]
    }
   ],
   "source": [
    "#  THIS IS THE PRIMARY VERSION TO USE FOR ALL STATES  #from WI 5/25/24\n",
    "print(\"This is a generic code for drawing a state's Home Districts for neutral map estimation\") #Jan'24\n",
    "print(\"In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\")\n",
    "import shapely\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "from shapely.ops import nearest_points, transform\n",
    "from shapely.affinity import translate, scale\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "from scipy.optimize import minimize, minimize_scalar\n",
    "import math\n",
    "import time\n",
    "import ast\n",
    "# from HDmethods import *   #I want to implement this, but my HDmethods require shapely functions\n",
    "# see https://stackoverflow.com/questions/66877728/proper-way-to-use-import-when-calling-external-function for a future workaround\n",
    "\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)  \n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "2ab4a3d5-f4e7-4aec-b81c-0e786cd35b57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def getWeightedAvgAndSD(LIST, WEIGHTS):  #from IN\n",
    "    nWeights = len(WEIGHTS)\n",
    "    normWeights = [WEIGHTS[i] / np.sum(WEIGHTS) for i in range(nWeights) ]\n",
    "    AVG = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        AVG += normWeights[i] * value\n",
    "    sumVar = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        sumVar += normWeights[i] * (value - AVG)**2\n",
    "    SD = sumVar ** 0.5     #don't need to normalize again since weights were normalized\n",
    "    return AVG, SD\n",
    "\n",
    "def squish(shapeList, cutLINE, farLINE, newFarLINE):\n",
    "    \"\"\"\n",
    "    This method compresses a list of shapes lying between the cutLINE and the farLINE)\n",
    "    such that the Polygons now lie between the cutLINE and the newFarLINE, which is closer to the cutLINE\n",
    "    Used to compress state outcroppings into a more compact state representation.\n",
    "     (I tried doing this point by point (see #'s), but this overdistorted geometries.)\n",
    "      (#This was a manual alternative to shapely.affinity.scale and .translate operations)\n",
    "       So instead, we run this AFTER established untransformed map topology, and \n",
    "        we simply scale and translate each unit.  This loses true connectivity.\n",
    "        Scaling is all lengths scale by compression of distance\n",
    "    The cut, far, and newFar LineString segments are preferably parallel\n",
    "    The method returns the modified shapes of all pollys\n",
    "    \"\"\"\n",
    "    if cutLINE.intersects(farLINE) or cutLINE.intersects(newFarLINE):\n",
    "        print(\"cutLine,farLine, newFarLine were\",cutLINE, farLINE, newFarLINE)\n",
    "        raise Exception(\"ERROR: the proposed cutLine intersects the current or proposed far line\")\n",
    "    newPollys = list()\n",
    "    for polly in shapeList:\n",
    "        pollyCP = polly.centroid\n",
    "        cutPt =     nearest_points(cutLINE,   pollyCP)[0]\n",
    "        farPt =     nearest_points(farLINE,   pollyCP)[0]\n",
    "        newFarPt =  nearest_points(newFarLINE,pollyCP)[0]\n",
    "        curr_cutX, curr_cutY =            pollyCP.x - cutPt.x, pollyCP.y -  cutPt.y\n",
    "        currFar_CutX , currFar_CutY  =    farPt.x - cutPt.x, farPt.y   -  cutPt.y\n",
    "        new_currFarX, new_currFarY =      newFarPt.x - farPt.x, newFarPt.y - farPt.y\n",
    "        newFar_CutX,  newFar_CutY =       newFarPt.x - cutPt.x, newFarPt.y   -  cutPt.y\n",
    "        distanceRatio = newFarPt.distance(cutPt)/farPt.distance(cutPt) #scale area ^length^2\n",
    "        XOFF,YOFF = 0., 0.\n",
    "        XFACT, YFACT = 1., 1.  #default = no stretch\n",
    "        # basic equation: (new-cut)/(curr-cut) = (newFar-cut)/(currFar - cut)\n",
    "        #  or: (new-curr)  = (curr-cut)*(newFar-currFar)/(currFar - cut)   # -1 to both sides\n",
    "        if currFar_CutX != 0:\n",
    "            XOFF =  curr_cutX * new_currFarX / currFar_CutX\n",
    "            XFACT =              newFar_CutX / currFar_CutX\n",
    "        if currFar_CutY != 0:\n",
    "            YOFF =  curr_cutY * new_currFarY / currFar_CutY\n",
    "            YFACT =              newFar_CutY / currFar_CutY\n",
    "        newPolly = translate(polly,xoff=XOFF,yoff=YOFF)\n",
    "        newPolly = scale(newPolly, xfact=XFACT, yfact=YFACT, origin='centroid')\n",
    "        newPollys.append( newPolly )       \n",
    "    return newPollys\n",
    "\n",
    "def getHDcp(TRACTCP,TRACTPOP, TRACTLIST, SPLITTRACTNO = -777,SPLITTRACTUSE = 1.):  #population centerpoint of a Home District or county (cluster)\n",
    "    cpx, cpy, sumPop = 0.,0., 0.\n",
    "    for tt in TRACTLIST:\n",
    "        USE = 1.\n",
    "        if tt ==    SPLITTRACTNO:\n",
    "            USE =   SPLITTRACTUSE\n",
    "        sumPop += USE*TRACTPOP[tt]\n",
    "        cpx +=    USE*TRACTPOP[tt] * TRACTCP[tt].x\n",
    "        cpy +=    USE*TRACTPOP[tt] * TRACTCP[tt].y\n",
    "    HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n",
    "    return HDCP_\n",
    "\n",
    "#  The below four methods are TO SNAP to HD SHAPES without any solving / iteration\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    The optional xScale parameter is the ratio of longitude to latitude lengths scales (<<1 for far from equator)\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def buildPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a 4-tri polygon from four HD wedges emanating from the centerpoint\n",
    "    Pt = [Point(0,0)]*12                      #xScale has same meaning as in buildWedge\n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW]  #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ]  \n",
    "        Pt[nW*2] =   Point(CP.x + RADII[nW]/XSCALE*math.cos(ccwAngle), CP.y + RADII[nW]*math.sin(ccwAngle) )\n",
    "        Pt[nW*2+1] = Point(CP.x + RADII[nW]/XSCALE*math.cos( cwAngle), CP.y + RADII[nW]*math.sin( cwAngle) )        \n",
    "    POLLY = Polygon([Pt[0],Pt[1],Pt[2],Pt[3],Pt[4],Pt[5],Pt[6],Pt[7] ])\n",
    "    return POLLY\n",
    "\n",
    "def buildArcPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a fuller polygon from four HD wedges emanating from the centerpoint\n",
    "    twoPi = 2. * 3.1415926   #xScale has same meaning as in buildWedge\n",
    "    POINTS = list()                   \n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW] % twoPi                 #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ] % twoPi \n",
    "        midAngle = [0.75*ccwAngle + 0.25*cwAngle , 0.25*ccwAngle + 0.75*cwAngle ]  #avoid s sampling cone center for wide angles\n",
    "        if abs (ccwAngle - cwAngle) > 3.1415926 :  #angles straddle angle=0\n",
    "            midAngle = [0.75*(ccwAngle - twoPi) + 0.25*cwAngle , 0.25*(ccwAngle -twoPi) + 0.75*cwAngle ]\n",
    "        angList = [ccwAngle] + midAngle + [cwAngle]\n",
    "        for ang in angList:\n",
    "            POINTS.append(Point(CP.x + RADII[nW]/XSCALE*math.cos(ang), CP.y + RADII[nW]*math.sin(ang) ) )\n",
    "                          \n",
    "    POLLY = Polygon(POINTS)\n",
    "    return POLLY\n",
    "\n",
    "def getPolyPop(POLLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points contained by a polygon\n",
    "    WPOP, WLIST = 0., list()\n",
    "    for tt in CANDIDATELIST:\n",
    "        if POLLY.contains(TRACTCP[tt]):\n",
    "            WPOP += TRACTPOP[tt]\n",
    "            WLIST.append(tt)\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonCPP(POLLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county in getPolyPop\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if POLLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonHCunits(POLLY, HC, UNITLIST, UNITCP, UNITPOP, ALLFUSEDCOUNTIES, AREAFRAC, COUNTYGEOM, \n",
    "                  NEIGHBORCOUNTYLIST, COUNTYUNITLIST):\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"unit counties\" are added as whole units if a sufficient area fraction is captured.  For non-unitCs, we probe each component vtd / tract\n",
    "    \"\"\"\n",
    "    UPOP, ULIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county before we called this method\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        #1/9/24 - DO WE NEED TO MODIFY THE ORDER BELOW TO AVOID CREATING HOLES AND ENCLAVES ??  #\n",
    "        \n",
    "        for c in latestClist:\n",
    "            for cc in list( set(NEIGHBORCOUNTYLIST[c]).difference(set(ALLFUSEDCOUNTIES)) ):\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        if cc+0.5 in UNITLIST:\n",
    "                            if POLLY.intersection(COUNTYGEOM[cc]).area >=  AREAFRAC[cc] * COUNTYGEOM[cc].area :\n",
    "                                intersectedClist.append(cc)   #for unit counties, intersections count only if they capture sufficient area\n",
    "                                newClist.append(cc)\n",
    "                                UPOP += UNITPOP[UNITLIST.index(cc+0.5)]\n",
    "                                ULIST.append( UNITLIST.index(cc+0.5) )\n",
    "                        else:                           \n",
    "                            intersectedClist.append(cc)\n",
    "                            newClist.append(cc)\n",
    "                            if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                                unitsToAdd = COUNTYUNITLIST[cc]\n",
    "                                UPOP += np.sum(  [ UNITPOP[uu] for uu in unitsToAdd ]  )\n",
    "                                ULIST += unitsToAdd\n",
    "                            else:\n",
    "                                for uu in COUNTYUNITLIST[cc]:\n",
    "                                    if POLLY.contains(UNITCP[uu]):\n",
    "                                        UPOP += UNITPOP[uu]\n",
    "                                        ULIST.append(uu)\n",
    "        latestClist = newClist.copy()\n",
    "        \n",
    "    return UPOP, ULIST\n",
    "\n",
    "def clusterSwell(CP_, CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, borderUnits, TGTPOP):\n",
    "    \"\"\"\n",
    "    This method grows a Home District by \"swelling\" from a corner county cluster.  First, we add the full cluster,\n",
    "     then we add closest neighbor units to the home district centerpoint CP_ until we reach the TGTPOP\n",
    "     new for MN 4/7/24 - don't enforce contiguity here -- too slow -- fix in cleanup stage instead\n",
    "     \"\"\"\n",
    "    hd_CCBdist = [CP_.distance(geo) for geo in CCBgeom]\n",
    "    CCBno = hd_CCBdist.index(np.min(hd_CCBdist))  #ID's the closest cluster to this HD center.  Should contain the HD, but rarely will not\n",
    "    unitNo = allUnits.index(CCBno+0.25)\n",
    "    newList, prevList, addedList, addedPop = [unitNo], [unitNo], [unitNo], unitPop[unitNo]  #seed with the cluster pop and unit\n",
    "    while addedPop < 0.99 * TGTPOP and len(newList) > 0:\n",
    "        tryList = getAdjoiners(addedList,unitNbrs)\n",
    "        tryDist = [ CP_.distance(unitCP[UU]) for UU in tryList ]\n",
    "        idx = np.argsort(tryDist)\n",
    "        idxNo, newList = 0, list()\n",
    "        haveNotAdded = True\n",
    "        while idxNo < len(tryList) and haveNotAdded:\n",
    "            UU = tryList[idx[idxNo]]\n",
    "            if unitPop[UU] + addedPop < 1.01 * TGTPOP :  #and wontEnclave(UU, addedList, unitNbrs, borderUnits) :  #we'll post-fix contig'y\n",
    "                newList.append(UU)\n",
    "                addedList.append(UU)\n",
    "                addedPop += unitPop[UU]\n",
    "                haveNotAdded = False\n",
    "            idxNo +=1\n",
    "        prevList = newList.copy()\n",
    "        \n",
    "    return addedPop, addedList\n",
    "            \n",
    "def getFakeHC(HDPOLLY, HC, CCBlist, countyGeom, MAP) :\n",
    "    \"\"\"\n",
    "    This method is for setting a fake home county for counties in corner clusters, so that county neighbors can be found budding from the \"home county\"\n",
    "    We pick the county in the cluster closest to the map center and intersecting the HDpoly.  This will be an active county on the cluster boundary   \n",
    "    \"\"\"\n",
    "    MAPcenter = MAP.centroid\n",
    "    for L in CCBlist:\n",
    "        if HC in L:\n",
    "            distList, cList = list(), list()\n",
    "            for C in L:\n",
    "                if HDPOLLY.intersects(countyGeom[C]):\n",
    "                    distList.append(countyGeom[C].distance(MAPcenter))\n",
    "                    cList.append(C)\n",
    "    fakeHC = cList[distList.index(np.min(distList)) ]\n",
    "    return fakeHC\n",
    "\n",
    "def getLongDist(CP1, CP2, xScale = 1.0): #distance between two points with EW distance scaled down by latitude\n",
    "    #LAT = MAP.centroid.y\n",
    "    #xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "    dist = ( xScale*xScale * (CP1.x - CP2.x)*(CP1.x - CP2.x)  +  (CP1.y - CP2.y)*(CP1.y - CP2.y) ) **0.5 \n",
    "    return dist\n",
    "\n",
    "# THESE ARE THE METHODS USED IN SOLVING HD SHAPES\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def getCountyWP(T, WEDGEPOLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points in a wedge excluding a point\n",
    "        WPOP, WLIST = 0., list()\n",
    "        for tt in CANDIDATELIST:\n",
    "            if tt != T and WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                WPOP += TRACTPOP[tt]\n",
    "                WLIST.append(tt)\n",
    "        return WPOP, WLIST\n",
    "\n",
    "def getNonCWP(WEDGEPOLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by an infinite polygonal wedge for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def isIncludedA(STARTa, ENDa, TESTa): #determines if a test angle is between a start and end angle (True)\n",
    "    \"\"\"\n",
    "    The start angle must be less than the end angle when placed on the [0, 2 pi] interval  (ccw convention)\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    STARTa, ENDa, TESTa = STARTa % (2.*pi), ENDa % (2.*pi), TESTa % (2.*pi)  #place on the 2pi interval\n",
    "    isIncludedA = False\n",
    "    if STARTa  > ENDa :  #included angle straddles east\n",
    "        if   TESTa  >= STARTa or TESTa < ENDa :  #near-east between the two\n",
    "            isIncludedA = True\n",
    "    else:\n",
    "        if TESTa >= STARTa and TESTa < ENDa :  #normal case\n",
    "            isIncludedA = True\n",
    "    return isIncludedA\n",
    "\n",
    "def getNonCWP_c(STARTANGL, ENDANGL, HC, TRACTCP,TRACTPOP,COUNTYGEOM,COUNTYPOP,COUNTYTRACTLIST,\n",
    "                                    NEIGHBORCOUNTYLIST, UUDIST, UUANGLE, WEDGEPOLY) :\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a polygonal wedge for counties contiguous with the Home County (no hop-overs),\n",
    "    EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    Unlike its getNonCWP vanilla parent, this one uses angles rather than infinite wedges for units (still wedges for counties)\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if isIncludedA(STARTANGL, ENDANGL, UUANGLE[tt]): #WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getExitAngle(CP,WEDGEPOLY, MAP, A0, A1):\n",
    "    \"\"\"\n",
    "    This code determines the orientation angle from a CenterPoint to the intersection of a wedge with an exterior boundary\n",
    "    If an intersection is not found, we just take the average angle of the wedge start/stop angles A0, A1 as this orientation angle\n",
    "    MAP must be a single polygon for this to work in the revised code (tho could run thru all polygons in MAP if a multipolygon)\n",
    "    \"\"\"\n",
    "    EXITANGLE = 0.5* (A0 + A1) #this will be the angle from the x-axis to the exit beeline\n",
    "    if WEDGEPOLY.intersects(MAP.exterior):\n",
    "        edgeLine = WEDGEPOLY.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "        closePoint = nearest_points(edgeLine,CP)[0]\n",
    "        dx = closePoint.x - CP.x       #this and below lines were indented in original code***\n",
    "        dy = closePoint.y - CP.y\n",
    "        EXITANGLE =  pi/2. * np.sign(dy)  #default in case dx=0\n",
    "        if (dx != 0. ):\n",
    "            EXITANGLE = math.atan(dy/dx) + random.uniform(-0.01,0.01) #add wiggle to avoid exact NESW orientation in gridded states\n",
    "            if dx < 0. :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                EXITANGLE = pi + EXITANGLE\n",
    "    return EXITANGLE # this reorients 0th wedge to face boundary's closest point\n",
    "\n",
    "def getNewAngles(mWP, tWP, ADP, LEVEL_L, MINADJRATIO, MAXANGLE, MAXANGLERATIO, nUNFILLEDWEDGES): \n",
    "    \"\"\"\n",
    "    This method classifies Home Districts based on how their post-reoriented equi-angle max wedge pops stack up vs targets,\n",
    "     then changes wedge angles in some situations to pick up more population along the boundary\n",
    "    If there is one wedge that will fall short of a quarter district pop, then we adjust angles if it is sufficiently short    \n",
    "    (Using \"HD2\" code, the opposite wedge's pop will be constrained as well.)\n",
    "    If there are two opposite-facing constrained wedges, we do not adjust angles\n",
    "    If there are two adjacent constrained wedges, we classify as a corner (no angle change) if the adjacent wedge is sufficiently constrained,\n",
    "      otherwise we treat as a single constrained wedge\n",
    "    If there are three constrained wedges, the angles are unchanged; we use the 4th wedge to pick up all pop\n",
    "    If all four wedges are constrained, there is an error and we raise a flag.\n",
    "    mWP is the quadlist of maxWedgePops we would get by extending each wedge to the MAP boundary\n",
    "    tWP is the quadlist of targetWedgePops\n",
    "    LEVEL_L is the non-dimensional distance to the boundary at which we no longer adjust wedge angles to drive more near-boundary pop\n",
    "    MAXANGLERATIO is the max ratio of the wide angle (facing the boundary) to the normal angle (e.g. 90deg for four wedges) ...\n",
    "    but this is truncated near-boundary to MAXANGLE (e.g. 1.8 pi/2 instead of increasing all the way to 1.9 pi/2)\n",
    "      NOTE THAT this code does not consider nearly-shorted wedges -- those are a separate method called later if all mWP's > tWP's\n",
    "    \"\"\"   \n",
    "    isChange = False   #default; we are NOT changing angles\n",
    "    printDebuggg = False\n",
    "    NWEDGES = len(mWP)\n",
    "    avgWedgeAngle = 2.*math.pi / NWEDGES\n",
    "    minW = mWP.index(np.min(mWP))  #index of the wedge with the lowest max wedge pop\n",
    "    oppW = int( int(minW + NWEDGES/2) % NWEDGES )  #and its opposing wedge\n",
    "    Lstar = ( mWP[minW] / (0.25*ADP) )**0.5  #shortest wedge's nondim'l distance to boundary\n",
    "    \n",
    "    case = \"keep same wedge angles\" #default; no unfillable wedges or all but one are unfillable\n",
    "    if nUNFILLEDWEDGES >= NWEDGES:\n",
    "        raise Exception(\"ERROR! ALL WEDGES ARE CONSTRAINED for tract\",t,\".  IMPOSSIBLE!!\")\n",
    "    if nUNFILLEDWEDGES == 1:\n",
    "        case = \"constrain opp wedge\"\n",
    "        if Lstar >= levelL :\n",
    "            case = \"keep same wedge angles\"  #not close enough to boundary to distort HD shape\n",
    "    if nUNFILLEDWEDGES == 0 or nUNFILLEDWEDGES == NWEDGES - 1:  #far from boundary or with only one wedge w/large pop\n",
    "        case = \"keep same wedge angles\"   \n",
    "    if nUNFILLEDWEDGES == 2:  #must determine if opposite or adjacent           \n",
    "        if mWP[oppW] < tWP[oppW]:  #shorted wedges oppose each other, so ...\n",
    "            case = \"keep same wedge angles\" #...the HD shape will naturally widen to pick up adjacent pop\n",
    "        else:  #we have 2 adjacent (non-opposing) shorted wedges... but how shorted?  ID the adjacent wedge\n",
    "            for nW in range(NWEDGES):\n",
    "                if mWP[nW] < tWP[nW] and nW != minW :\n",
    "                    adjW = nW  #this is the adjacent wedge\n",
    "                    adjRatio = mWP[adjW] / tWP[adjW]\n",
    "            if adjRatio < minAdjRatio:  #we're close to a corner (this adjacentWedge is significantly constrained)\n",
    "                case = \"keep same wedge angles\"\n",
    "                if printDebuggg :\n",
    "                    print(\"Not adjusting wedge angles for tract\",t,\"as 2nd-sparsest wedge\",adjW,\"has pop\",mWP[adjW] )\n",
    "            else:\n",
    "                case = \"constrain opp wedge\"  #we will set the angles and target pops as if the adj wedge were not constrained\n",
    "    \n",
    "    if case == \"keep same wedge angles\":\n",
    "        isChange = False\n",
    "        WEDGEANGLE = [avgWedgeAngle for w in range(NWEDGES) ]\n",
    "\n",
    "    if case == \"constrain opp wedge\":  #Here, we modify wedge angles.  MWP's will be recalc'd outside the method\n",
    "        isChange = True  \n",
    "        wideAngle = min(MAXANGLE, avgWedgeAngle*max(  1,( 1.+(MAXANGLERATIO-1.)*(LEVEL_L - Lstar)/(LEVEL_L - 0.) )  ) )\n",
    "        WEDGEANGLE = [(2.*pi - 2. * wideAngle)/ (NWEDGES - 2.) for w in range(NWEDGES) ]  \n",
    "        WEDGEANGLE[minW] = wideAngle\n",
    "        WEDGEANGLE[oppW] = wideAngle\n",
    "    \n",
    "    return isChange, WEDGEANGLE\n",
    "\n",
    "def rebalanceTWPs(MWP, TWP, BARREDLIST=list()):\n",
    "    \"\"\"\n",
    "    This method iteratively increases targetWedgePops to accommodate wedges that have maxWedgePop < targetWedgePop.\n",
    "    The wedgePop gap is distributed equally among wedges that have some capacity and are not BARRED (e.g. an opposite wedge in HD2 method)\n",
    "     (If this pushes some wedges over capacity, this will be fixed in a later run through loop inside this method)\n",
    "    We also flag which wedges \"will fill\" = have sufficient capacity after the rebalancing of the targets to not use the entire maxWedgePoly\n",
    "    \"\"\"\n",
    "    DEBUGrTWP = False\n",
    "    NWEDGES = len(MWP)\n",
    "    unorderedCapacity = [MWP[w] - TWP[w] for w in range(NWEDGES) ]\n",
    "    idx = np.argsort(unorderedCapacity)\n",
    "    #for nn in range(NWEDGES):\n",
    "    #    print(MWP[idx[nn]])\n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        for nn in range(NWEDGES):\n",
    "            print(\"barred list, orig MWP, TWP\",BARREDLIST, r3(MWP[nn]),r3(TWP[nn]) )\n",
    "    \n",
    "    for nn in range(NWEDGES):  #going from least to most maxWedgePop here ...\n",
    "        nW = idx[nn]\n",
    "        if MWP[nW] < TWP[nW]: #need to redistribute extra target to higher-capacity wedges ...\n",
    "            wedgePopGap = TWP[nW] - MWP[nW]\n",
    "            TWP[nW] = MWP[nW]  #max out this wedge\n",
    "            nReceivers = 0.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....\n",
    "                    nReceivers += 1.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....                    \n",
    "                    TWP[WW] += wedgePopGap / nReceivers  #... so give it equal share of the gap, even if this goes over; we'll correct in later loop\n",
    "                    \n",
    "    WILLFILL = [1]*NWEDGES\n",
    "    for nW in range(NWEDGES):\n",
    "        if MWP[nW] < 1.001* TWP[nW]:  #required pop nearly or truly requires the entire wedge\n",
    "            WILLFILL[nW] = 0 \n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        print(\"adjusted TWP's are\",TWP)\n",
    "    return TWP, WILLFILL\n",
    "\n",
    "def solveWedge(nontractTWP,t, hC, STARTANGLE, ENDANGLE, MAXD, TOLERPOPS, tractCP, tractPop,\n",
    "               countyTractList, countyGeom, countyPop, neighborCountyLIST,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    #### NO LONGER USED; SEE FASTER SOLVEWEDGEB BASED ON UNIT-UNIT DISTANCES  *********\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop (which excludes the home tractPop)\n",
    "    The wedge has a fixed starting and ending angle.  Its maximum diameter is angle-related to max diameter of the state map\n",
    "    It uses bisection, NOT scipy minimize to minimize the square error in the wedge pop vs. target\n",
    "    The method passes back the final wedge pop and list of tracts in the wedge\n",
    "    \"\"\"\n",
    "    chgLoopNo, maxLoopNo = 10., 22.  #when we will start relaxing the pop tolerance, and when we give up\n",
    "    includedAngl = ENDANGLE - STARTANGLE\n",
    "    guessedR = MAXD / math.cos(0.5*includedAngl)  #max possible wedge radius, accounting for possible wide angle\n",
    "    popTol = TOLERPOPS[0]  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    #                      We loosen toward half the MAX tractPop if not converging\n",
    "    \n",
    "    offsetPop = 88888888.  #to force a reduction the first time through the loop\n",
    "    dr = guessedR\n",
    "    loopNo = 0\n",
    "    while abs(offsetPop) > popTol and loopNo < maxLoopNo:\n",
    "        loopNo +=1\n",
    "        popTol = max(TOLERPOPS[0], TOLERPOPS[0] + (TOLERPOPS[1] - TOLERPOPS[0]) * (loopNo - chgLoopNo) / (maxLoopNo - chgLoopNo) )\n",
    "        dr = 0.5*dr\n",
    "        guessedR -= dr*np.sign(offsetPop)\n",
    "        \n",
    "        guessedPoly = buildWedge(tractCP[t],STARTANGLE, ENDANGLE, guessedR,XSCALE) \n",
    "        iCP, iClist =  getCountyWP(t,guessedPoly, tractCP, tractPop, countyTractList[hC])\n",
    "        nonCP, nonClist = getNonCWP(guessedPoly, hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyLIST)\n",
    "        offsetPop = iCP + nonCP - nontractTWP\n",
    "        #print(t,loopNo,r5(guessedR),int(iCP),int(nonCP),r3(offsetPop+nontractTWP),r3(nontractTWP),\"t,loop,R,iCP,nonCP,pop,target\")\n",
    "        if loopNo >= maxLoopNo-1:\n",
    "            print(\"WARNING. Looped\",loopNo,\"times for t,angles,R\",t,r3(STARTANGLE),r3(ENDANGLE),r5(guessedR),\n",
    "                  \"wedgePop offset, target are\",int(offsetPop), int(nontractTWP) )    \n",
    "    finalPop = iCP + nonCP\n",
    "    finalList = iClist + nonClist\n",
    "    return finalPop, finalList, guessedR, loopNo\n",
    "\n",
    "#above = bisection.  Below solveWedgeB uses static unit-unit distances\n",
    "# Also tried scipy minimize, which doesn't seem any faster\n",
    "\n",
    "def solveWedgeB(nontractTWP,T, HC, POLLY, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that intersect,\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2x2 array\n",
    "    \"\"\"\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    \n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList = 0, 0, list()\n",
    "    notFull = True\n",
    "    while notFull :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist    \n",
    "\n",
    "def solveWedgeC(nontractTWP,T, HC, POLLY, STARTANGL, ENDANGL, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST, UUANGLE):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that\n",
    "    fall within the angle range (in lieu of wedgeB's check for intersection),\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2D array\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    STARTANGL, ENDANGL = STARTANGL % (2.*pi), ENDANGL % (2.*pi)\n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList, latestDist = 0, 0, list(), 0.00001  #dummy value\n",
    "    notFull = True\n",
    "    while notFull and i < len(UUDIST) - 2 :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if isIncludedA(STARTANGL, ENDANGL, UUANGLE[TT]) : #POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist \n",
    "\n",
    "\n",
    "def getPartialTract(t, HDTRACTLIST, offsetPop, UUDIST, tractPop, neighborLIST):\n",
    "    \"\"\"\n",
    "    If offsetPop is >0, this method identifies the tract with centroid farthest from Home t that can jettison at least offsetPop ...\n",
    "    ... by slicing out part of this tract.\n",
    "    If offsetPop <0, we ID a tract CLOSEST to Home t among neighbors of in-HD tracts to pick up a partial\n",
    "    We return the tractID and its new partial use\n",
    "    We have updated this method to pass the uuDist slice for tract t instead of computing tract distances inside here\n",
    "    \"\"\"\n",
    "    debugGPT = False\n",
    "    NTRACTS = len(tractCP)\n",
    "    bigDist = 9999999.\n",
    "    qualifyingTractList = list()\n",
    "    isWholeTract = False\n",
    "    if offsetPop > 0:\n",
    "        homeDist = [0.]*NTRACTS  #default = 0 to not get picked\n",
    "        for TT in HDTRACTLIST:\n",
    "            if tractPop[TT] > offsetPop:\n",
    "                qualifyingTractList.append(TT)\n",
    "        for TT in qualifyingTractList:\n",
    "            homeDist[TT] = UUDIST[TT]\n",
    "        if len(qualifyingTractList) == 0:  #very rare case where all in-HD tracts are too small.  Jettison nearly the whole farthest one\n",
    "            isWholeTract = True\n",
    "            for TT in HDTRACTLIST:\n",
    "                if tractPop[TT] > 0.9*np.average(tractPop): #set arbitrary min qualifying pop\n",
    "                    homeDist[TT] = UUDIST[TT]\n",
    "        targetT = homeDist.index(np.max(homeDist))\n",
    "        partialUseFrac = max(0.0001,(tractPop[targetT] - offsetPop)/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(\"positive offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    else: #pick up a partial\n",
    "        homeDist = [bigDist]*NTRACTS\n",
    "        for TT in HDTRACTLIST:\n",
    "            for TTT in neighborList[TT]:\n",
    "                if TTT not in qualifyingTractList and TTT not in HDTRACTLIST and tractPop[TTT] > abs(offsetPop):\n",
    "                    qualifyingTractList.append(TTT)\n",
    "        for TTT in qualifyingTractList:\n",
    "            homeDist[TTT] = UUDIST[TTT]\n",
    "        if len(qualifyingTractList) == 0 :  #rare case where most nearby tracts are small.  Add nearly the whole biggest one\n",
    "            isWholeTract = True\n",
    "            maxPop = 0.\n",
    "            targetT = -999\n",
    "            for TT in HDTRACTLIST:\n",
    "                for TTT in neighborList[TT]:\n",
    "                    if TTT not in qualifyingTractList and TTT not in HDTRACTLIST : # we'll take just one, even if doesn't fill gap\n",
    "                        qualifyingTractList.append(TTT)\n",
    "            for TTT in qualifyingTractList:\n",
    "                if tractPop[TTT] > maxPop:\n",
    "                    targetT = TTT\n",
    "                    maxPop = tractPop[targetT]\n",
    "        else:\n",
    "            targetT = homeDist.index(np.min(homeDist))\n",
    "        partialUseFrac = min(0.9999, -1.* offsetPop/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(t,\"'s negative offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    \n",
    "    return targetT, partialUseFrac\n",
    "\n",
    "def getAdjoiners(UNITLIST, UNITNBRS): #returns all units that neighbor a UNITLIST but are not in the UNITLIST \n",
    "    allNbrs = list()\n",
    "    for U in UNITLIST:\n",
    "        allNbrs = allNbrs + UNITNBRS[U]\n",
    "    adjoiners = list( set(allNbrs).difference(set(UNITLIST)) )\n",
    "    return adjoiners\n",
    "\n",
    "def get2nbrs(ULIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    Get the combined list of first- and second-level neighbors of a LIST, using neighborList connectivity.  Excludes the source list\n",
    "    \"\"\"\n",
    "    firstList =  getAdjoiners(ULIST, UNITNBRS)\n",
    "    secondList = getAdjoiners(firstList, UNITNBRS)\n",
    "    twoLevelList = list( set(firstList + secondList).difference(set(ULIST) ) )\n",
    "    return twoLevelList\n",
    "\n",
    "def getContigFromStarter(starter, VLIST, NEIGHBORLIST):  #all contiguous units in VLIST, starting from starter unit\n",
    "    foundList, newList, prevList = [ starter ], [ starter ], [ starter ]\n",
    "    while len(newList) > 0:  #stop looping if no new neighbors found on previous loop\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()        \n",
    "    return foundList   \n",
    "\n",
    "def isContiguous(VLIST,NEIGHBORLIST,returnBiggestPiece=False):\n",
    "    \"\"\"\n",
    "    This method determines if all items in a list form a continuous chain of neighbors based on the passed neighborlist\n",
    "    Empty lists are considered contiguous.  If discontiguous, a less-than-majority piece list is returned,\n",
    "     unless giveBig=True, in which case we return the biggest piece\n",
    "    \"\"\"    \n",
    "    isUnbroken, newList, foundList = True, list(), list()\n",
    "    if len(VLIST) > 0:\n",
    "        foundList, newList, prevList = [ VLIST[0] ], [ VLIST[0] ], [ VLIST[0] ]\n",
    "    while len(newList) > 0:  #this round's list of neighbors\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()      \n",
    "    pickedPieceList = foundList.copy()  #default contiguous sublist to pass back to main\n",
    "    \n",
    "    if len(foundList) < len(VLIST):  #not contiguous\n",
    "        isUnbroken  = False\n",
    "        pieceLists, remainingList = [foundList], list( set(VLIST).difference(set(foundList) ) )\n",
    "        if returnBiggestPiece == True:  #we were asked for the biggest piece, not a random small one, so we must find them all\n",
    "            if len(foundList) < 0.5*len(VLIST):\n",
    "                while len(remainingList) > 0:\n",
    "                    newList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                    pieceLists.append(newList)\n",
    "                    remainingList = list(set(remainingList).difference(set(newList)))\n",
    "                pieceLengths = [len(pL) for pL in pieceLists]\n",
    "                pickedPieceList = pieceLists[ pieceLengths.index(np.max(pieceLengths)) ]\n",
    "            \n",
    "        else: #quickly return a contiguous sub-list that's at most half the total units in the list\n",
    "            if len(foundList) > 0.5*len(VLIST): #pick a different piece; this one is the majority\n",
    "                pickedPieceList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                \n",
    "    return isUnbroken, pickedPieceList\n",
    "\n",
    "def enclaveCheck(UNITLIST,UNITNBRS,maxLoops=4):\n",
    "    \"\"\"\n",
    "    This method determines if a list of units has an unbroken boundary AND whether its complement has an unbroken boundary\n",
    "    If the complement boundary is broken, there is an enclave or the district is so disconnected its adjoining units aren't contiguous\n",
    "    The method returns contiguity of boundary and of its adjoiners.  Also returns the lists of boundary and complement-boundary units\n",
    "      If either of these is broken, only a contiguous sublist is returned that is guaranteed to be smaller than half (for finding enclaves)\n",
    "      maxLoops is the number of loops for expanding neighbors-of-neighbors for contiguity of the units outside the passed unit-list\n",
    "    \"\"\"\n",
    "    UNITSET = set(UNITLIST)\n",
    "    ADJLIST =  get2nbrs(UNITLIST, UNITNBRS)  #in case direct adjoiners are only queen-adjacent\n",
    "    #adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\n",
    "    #BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\n",
    "    BDRYLIST = list (set(get2nbrs(ADJLIST,UNITNBRS)).intersection(UNITSET) )  #in case inHD boundary is only queen-adjacent\n",
    "    noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)  \n",
    "    unbroken, smallPieceList = isContiguous(BDRYLIST, UNITNBRS)\n",
    "    if not unbroken: #could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\n",
    "        unbroken, smallPieceList = isContiguous(UNITLIST, UNITNBRS)  #slower check for full district, not just its boundary \n",
    "    nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "    while nLoops <= maxLoops and not noEnclave:\n",
    "        nLoops +=1\n",
    "        newSet = set(ADJLIST)\n",
    "        for UU in ADJLIST:\n",
    "            newSet = newSet.union( set(UNITNBRS[UU]).difference(UNITSET) )\n",
    "        ADJLIST = list(newSet)\n",
    "        noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)\n",
    "    #isJiggy = noEnclave and unbroken\n",
    "    return unbroken, noEnclave, smallPieceList, enclaveList\n",
    "\n",
    "def avoidedEnclave(UNITLIST, UNITNBRS, ADDLIST,MAXLOOPS=15):  #20 is arbitrary, but does suppress slender chains\n",
    "    \"\"\"\n",
    "    This method (new for WI 5-23-24) is designed as a shorter check than enclaveCheck for legitimacy of adding ADDLIST to UNITLIST\n",
    "    It tries to find contiguity among all ADDLIST's neighbors that won't be in the UNITLIST after the add.\n",
    "    It does this in successive loops, growing from one non-UNITLIST neighbor in loops to all its non-UL neighbors\n",
    "    If success before MAXLOOPS, it returns True (i.e. this list addition avoided forming an enclave), else it returns False\n",
    "        Note that this method will not usually allow a UNITLIST that doesn't currently touch map border to start touching it\n",
    "        (too many loops to go all the way around the UNITLIST to find the complement's connection)\n",
    "    \"\"\"   \n",
    "    willBeInUnitSet = set(UNITLIST+ADDLIST)\n",
    "    nbrsOfADDLIST = set()\n",
    "    for U in ADDLIST:\n",
    "        nbrsOfADDLIST = nbrsOfADDLIST.union(set(UNITNBRS[U]))  #these are the neighbors of the units we plan to add to UNITLIST\n",
    "    nbrsOfADDLIST = nbrsOfADDLIST.difference(willBeInUnitSet)\n",
    "    if len(nbrsOfADDLIST) == 0:\n",
    "        raise Exception(\"ERROR! Attempted to add list\",ADDLIST,\"but it should have been flagged previously as an enclave\")\n",
    "    firstNo = next(iter(nbrsOfADDLIST))  #pick one of these non-UL neighbors as a starter\n",
    "    firstGroup = getContigFromStarter(firstNo, nbrsOfADDLIST, UNITNBRS) #first find all neighbors that directly connect\n",
    "    foundNbrSet, foundTotalSet, newFoundSet = set(firstGroup),set(firstGroup),set(firstGroup)\n",
    "    LOOPNO = 0\n",
    "    stillConnect = False\n",
    "    if len(foundNbrSet) == len(nbrsOfADDLIST):  #ALL non-UNITLIST neighbors still DIRECTLY connect after the add\n",
    "        stillConnect = True\n",
    "    while len(newFoundSet) > 0 and LOOPNO <= MAXLOOPS and not stillConnect:\n",
    "        LOOPNO +=1\n",
    "        lastFoundSet = newFoundSet\n",
    "        newFoundSet = set()\n",
    "        for U in lastFoundSet:\n",
    "            newFoundSet = newFoundSet.union(set(UNITNBRS[U]))\n",
    "        newFoundSet = newFoundSet.difference(willBeInUnitSet)\n",
    "        \n",
    "        foundTotalSet = foundTotalSet.union(newFoundSet)\n",
    "        foundNbrSet = foundTotalSet.intersection(nbrsOfADDLIST)  #running list of complement-connectable non-UL neighbors of ADDLIST\n",
    "        if len(foundNbrSet) == len(nbrsOfADDLIST):  #ALL non-UNITLIST neighbors still connect after the add\n",
    "            stillConnect = True\n",
    "    return stillConnect\n",
    "              \n",
    "\n",
    "def avoidedIsland(UNITLIST, UNITNBRS, SHEDLIST,MAXLOOPS=15):\n",
    "    \"\"\"\n",
    "    analogous to avoidedEnclave but for shedding a list from UNITLIST; we look to connect in-UNITLIST neighbors after the proposed shed\n",
    "    \"\"\"\n",
    "    shrunkSet = set(UNITLIST).difference(set(SHEDLIST))  #shorthand for the proposed unitlist after shedding\n",
    "    nbrsOfSHEDLIST = set()\n",
    "    for U in SHEDLIST:\n",
    "        nbrsOfSHEDLIST = nbrsOfSHEDLIST.union(set(UNITNBRS[U]))\n",
    "    nbrsOfSHEDLIST = nbrsOfSHEDLIST.intersection(shrunkSet)\n",
    "    giveUp = False\n",
    "    if len(nbrsOfSHEDLIST) == 0:\n",
    "        giveUp = True  #rarely, the shedlist is contiguous but creates a weird contiguity problem (surrounds?).  Punt.\n",
    "        return False\n",
    "        #raise Exception(\"ERROR! Attempted to shed list\",SHEDLIST,\"but it was never connected to the rest of the unit list\")\n",
    "    firstNo = next(iter(nbrsOfSHEDLIST))  #pick one of these still-in-UL neighbors as a starter   \n",
    "    firstGroup = getContigFromStarter(firstNo, nbrsOfSHEDLIST, UNITNBRS) #first find all neighbors that directly connect\n",
    "    foundNbrSet, foundTotalSet, newFoundSet = set(firstGroup),set(firstGroup),set(firstGroup)\n",
    "    LOOPNO = 0\n",
    "    stillConnects = False\n",
    "    if len(foundNbrSet) == len(nbrsOfSHEDLIST):  #ALL in-UNITLIST neighbors of the shedlist still DIRECTLY connect after the shed\n",
    "        stillConnects = True\n",
    "    while len(newFoundSet) > 0 and LOOPNO <= MAXLOOPS and not stillConnects and not giveUp:\n",
    "        LOOPNO +=1\n",
    "        lastFoundSet = newFoundSet\n",
    "        newFoundSet = set()\n",
    "        for U in lastFoundSet:\n",
    "            newFoundSet = newFoundSet.union(set(UNITNBRS[U]))\n",
    "        newFoundSet = newFoundSet.intersection(shrunkSet)\n",
    "        \n",
    "        foundTotalSet = foundTotalSet.union(newFoundSet)\n",
    "        foundNbrSet = foundTotalSet.intersection(nbrsOfSHEDLIST)\n",
    "        if len(foundNbrSet) == len(nbrsOfSHEDLIST):  #all in-UNITLIST neighbors still connect after the shed\n",
    "            stillConnects = True\n",
    "    return stillConnects\n",
    "\n",
    "def getBdryNonEdgers(UNITLIST, UNITNBRS): #all in-district boundary units that neighbor a non-district unit\n",
    "    ALLnonHDnbrs = set()\n",
    "    for UUU in UNITLIST:\n",
    "        ALLnonHDnbrs = ALLnonHDnbrs.union(set(UNITNBRS[UUU])).difference(set(UNITLIST))\n",
    "    BdryNonEdgerSet = set()\n",
    "    for UUU in UNITLIST:\n",
    "        if len(set(UNITNBRS[UUU]).intersection(ALLnonHDnbrs)) > 0:\n",
    "            BdryNonEdgerSet.add(UUU)\n",
    "    return list(BdryNonEdgerSet)\n",
    "\n",
    "def getEnclaveLists(UNITLIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    finds ALL units enclaved by a UNITLIST, parsed into lists of contiguous pieces\n",
    "    We assume the largest contiguous complement to the UNITLIST is not an enclave, but rather the majority of the HD complement\n",
    "    if the UNITLIST's complement (all couldBeEnclaved) is contiguous, the returned list will be blank\n",
    "    \"\"\"\n",
    "    eSets, nU = list(), len(UNITNBRS)\n",
    "    offmapList = list()\n",
    "    for i,L in enumerate(UNITNBRS):\n",
    "        if len(L) == 0:\n",
    "            offmapList.append(i)  #offmap list are units with no neighbors (were surrounded)\n",
    "    complementSet = set([i for i in range(nU)] ).difference( set(UNITLIST + offmapList) )    \n",
    "    remaining2nbrs = get2nbrs(UNITLIST, UNITNBRS)\n",
    "    \n",
    "    isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS) #quicker than contig check on entire complement\n",
    "    while not isContig:  #this will kick out before writing the final sublist = map majority\n",
    "        starter = shortList[0]\n",
    "        newEnclaveList = getContigFromStarter(starter, list(complementSet), UNITNBRS)\n",
    "        eSets.append(set(newEnclaveList))\n",
    "        remaining2nbrs = list(set(remaining2nbrs).difference(set(shortList)) )\n",
    "        isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS)\n",
    "        \n",
    "        # couldBeEnclaved = list( set(couldBeEnclaved).difference(set(newEnclaveList)) )  #revised 18-Feb-24 -- was slower\n",
    "    fused_eSets = set()\n",
    "    for i, eSet in enumerate(eSets):\n",
    "        fused_eSets = fused_eSets.union(eSet)\n",
    "        for j in range(i+1, len(eSets) ) :\n",
    "            eSets[j] = eSets[j].difference(eSet)  #in case contiguity occurs, but not in 2-neighbor list; avoid double-count\n",
    "    remnant_eSet = complementSet.difference(fused_eSets) \n",
    "    eLengths = [len(eSet) for eSet in eSets]\n",
    "    if len(eSets) > 0:\n",
    "        if len(remnant_eSet) < np.max(eLengths):  #exchange the small remnant for the biggest found \"enclave\" (usually the major complement) \n",
    "            eSets[eLengths.index(np.max(eLengths))] = remnant_eSet.copy()\n",
    "    eLists = [list(eSet) for eSet in eSets]\n",
    "    return eLists\n",
    "\n",
    "def wontEnclave(proposedU, dList, NBRLIST, mapBDRYLIST):\n",
    "    \"\"\"\n",
    "    This method checks if adding a proposedU to a dList (list of units in a district) will create an \"enclave\" of units in the dList's complement\n",
    "    via two problems:  A) the dList will now have a discontiguous set of units on the map boundary.  The full map boundary list is mapBDRYLIST\n",
    "      B) The non-dList neighbors of dList units aren't contiguous\n",
    "    The method doesn't require that the dList wontEnclave without the proposedU included; it just checks the proposedU + dList combination\n",
    "    It also doesn't check that the dList is itself contiguous with or without proposedU\n",
    "    In that sense, it is more limited than enclaveCheck\n",
    "    \"\"\"\n",
    "    wontEnclave = True\n",
    "    newList, newSet = dList + [proposedU], set(dList + [proposedU])\n",
    "    unitsOnBoundary = list( set(mapBDRYLIST).intersection(newSet) )\n",
    "    wontEnclave, __ = isContiguous(unitsOnBoundary,NBRLIST)  #first check - does district touch MAP boundary in multiple places? (quick FAIL)\n",
    "    if wontEnclave: #slower check below for internal enclaves\n",
    "        adjoiners = get2nbrs(newList, NBRLIST)  #2-level to protect for queen adjacency in boundary        \n",
    "        wontEnclave, __ = isContiguous(adjoiners,NBRLIST)\n",
    "        if not wontEnclave:\n",
    "            nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "            maxLoops, ADJLIST = 6, adjoiners #unlike enclaveCheck, here we just arbitrarily set a number of loops for search expansion\n",
    "            while nLoops <= maxLoops and not wontEnclave:\n",
    "                nLoops +=1\n",
    "                adjSet = set(ADJLIST)  #align nomenclature w enclaveCheck\n",
    "                for UU in ADJLIST:\n",
    "                    adjSet = adjSet.union( set(NBRLIST[UU]).difference(set(newList)) )\n",
    "                ADJLIST = list(adjSet)\n",
    "                wontEnclave, __ =   isContiguous(ADJLIST, NBRLIST)\n",
    "        \n",
    "    return wontEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d041d328-83e0-45db-a869-e0956c23ee26",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the state postal code; e.g. OH  MO\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, enter 1 below to use mo_pl2020_vtd.dbf as this state's population data file\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attempting to read in state unit geometries.  Stand by ...\n",
      "I read in the Census popn data. MO has 6154913 peeps and is divided into 4604 shapes.\n"
     ]
    }
   ],
   "source": [
    "STATE = str(input(\"Enter the state postal code; e.g. OH \")).upper()\n",
    "popFilename = STATE.lower()+\"_pl2020_vtd.dbf\"\n",
    "print(\"OK, enter 1 below to use\",popFilename,\"as this state's population data file\")\n",
    "enter1 = input(\" \")\n",
    "if int(enter1) != 1:\n",
    "    popFilename = input(\"OK, then enter the pop data file.  Typically a xx_pl2020_vtd.dbf\")\n",
    "print(\"attempting to read in state unit geometries.  Stand by ...\")\n",
    "tractPopFile = gpd.read_file(\"state_map_files/\"+popFilename)\n",
    "trueTractGeom = tractPopFile['geometry']\n",
    "nTracts = len(trueTractGeom)\n",
    "tractPop = tractPopFile['P0010001']\n",
    "statePop = np.sum(tractPop)\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "print(\"I read in the Census popn data.\",STATE,\"has\",statePop,\"peeps and is divided into\",nTracts,\"shapes.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bfdea9d-b0ea-497a-a556-6b488a3e333e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the number of districts in the state.  My guess is 8 8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 115 counties in MO .  I will assign them county Nos 0 - 114\n"
     ]
    }
   ],
   "source": [
    "tractGeom = trueTractGeom.copy()   #for some states, we will modify geom, so preserve original\n",
    "#tractNAME20 = tractPopFile['NAME20']\n",
    "tractPop = tractPopFile['P0010001']\n",
    "inputfileCountyNo = tractPopFile['COUNTYFP20']\n",
    "vtdGEOID20 =  tractPopFile['GEOID20'] \n",
    "nDistricts = int(round(statePop/760000,0))\n",
    "nCutDistricts = 0\n",
    "nDistricts = int(input(\"Enter the number of districts in the state.  My guess is \"+str(nDistricts)))\n",
    "tractArea = [tractGeom[t].area for t in range(nTracts)]\n",
    "trueTractCP =   [tractGeom[t].centroid for t in range(nTracts)]\n",
    "tractCP = trueTractCP.copy()  #for some states, we move secluded corners into the map, so save the true data\n",
    "tractCPx =  [tractCP[t].x for t in range(nTracts)]\n",
    "tractCPy =  [tractCP[t].y for t in range(nTracts)]\n",
    "inputCountyNumbers = list()\n",
    "for t in range(nTracts):\n",
    "    if inputfileCountyNo[t] not in inputCountyNumbers:\n",
    "        inputCountyNumbers.append(inputfileCountyNo[t])\n",
    "nCounties = len(inputCountyNumbers)\n",
    "print(\"There appear to be\",nCounties,\"counties in\",STATE,\".  I will assign them county Nos 0 -\",int(nCounties-1))\n",
    "sortedICNs = list(np.sort(inputCountyNumbers))\n",
    "countyNo = [sortedICNs.index(inputfileCountyNo[t]) for t in range(nTracts) ]\n",
    "\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adffc7a4-a78c-477f-8ea4-7f8e5109fa2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now build the county-by-county geometries, hoping there are no islands\n",
      "Building county geom for county 0\n",
      "Building county geom for county 20\n",
      "Building county geom for county 40\n",
      "Building county geom for county 60\n",
      "Building county geom for county 80\n",
      "Building county geom for county 100\n",
      "Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop < 4.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 0 unpopulated coastal tracts.\n"
     ]
    }
   ],
   "source": [
    "print(\"We will now build the county-by-county geometries, hoping there are no islands\")\n",
    "skipList = list()  #a list of units we previously know to skip in the map\n",
    "isAlteredCounty = [False for c in range(nCounties)]\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "countyPop =       [0. for c in range(nCounties)]\n",
    "countyGeom =      [dummyPoly for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    if t not in skipList:\n",
    "        c = countyNo[t]\n",
    "        countyTractList[c].append(t)\n",
    "        countyPop[c] += tractPop[t]\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Building county geom for county\",c)\n",
    "    for t in countyTractList[c]:\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            countyGeom[c] = tractGeom[t]\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "origMAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    origMAP = origMAP.union(countyGeom[c])\n",
    "    if countyGeom[c] == dummyPoly:\n",
    "        print(\"WARNING! county\",c,\"is empty!\")\n",
    "    else:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "minTractPop = 4.5\n",
    "print(\"Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop <\",minTractPop)\n",
    "plt.show()\n",
    "if origMAP.geom_type == dummyPoly.geom_type:\n",
    "    mapExterior = origMAP.exterior\n",
    "else:\n",
    "    for i,geo in enumerate(origMAP.geoms):\n",
    "        if i == 0:\n",
    "            mapExterior = geo.exterior\n",
    "        else:\n",
    "            mapExterior = mapExterior.union(geo.exterior)\n",
    "\n",
    "for c in range(nCounties):\n",
    "    for t in countyTractList[c]:\n",
    "        if tractPop[t] < minTractPop:\n",
    "            if tractGeom[t].intersects(mapExterior):\n",
    "                isAlteredCounty[c] = True\n",
    "                skipList.append(t)\n",
    "print(\"There appear to be\",len(skipList),\"unpopulated coastal tracts.\")\n",
    "if len(skipList) > 0:\n",
    "    print(\"Lets plot them and their parent counties\")\n",
    "    for t in skipList:\n",
    "        plotPoly(tractGeom[t])\n",
    "        plotCenter(t,tractGeom[t],6)\n",
    "    plotPoly(origMAP,0.2)\n",
    "    for c in range(nCounties):\n",
    "        if isAlteredCounty[c]:\n",
    "            plotPoly(countyGeom[c])\n",
    "    plt.show()\n",
    "trueMAP = origMAP  #use these terms interchangeably"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a6c93c4-af08-47d5-90f7-dc0b4513aad5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last chance to alter the skipList, otherwise the above 0 units will be axed\n",
      "here is the proposed skipList []\n",
      "here is the final skipList []\n"
     ]
    }
   ],
   "source": [
    "print(\"Last chance to alter the skipList, otherwise the above\",len(skipList),\"units will be axed\")\n",
    "print(\"here is the proposed skipList\", skipList)\n",
    "#skipList = [146, 3084] #...  #alter here if needed - nothing for MO\n",
    "print(\"here is the final skipList\", skipList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2989f381-466b-4882-bf1d-cfa37a7ba8ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now preserve the original county unit lists and geoms, then rebuild as needed\n",
      "Here is the revised state county map after eliminating coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excellent!  We have no populated islands.  SKIP the below code block.\n",
      "we will scale longitudinal distance by 0.78447\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now preserve the original county unit lists and geoms, then rebuild as needed\")\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "trueCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "for c in range(nCounties):\n",
    "    trueCountyTractList[c] = countyTractList[c].copy()\n",
    "    if isAlteredCounty[c]:\n",
    "        print(\"removing coastal units from countyNo\",c)\n",
    "        countyTractList[c] = list()\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in trueCountyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                countyTractList[c].append(t)\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            print(\"Uh oh! county\",c,\"didn't have any usable tracts\")\n",
    "MAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c])\n",
    "    plotCenter(c,countyGeom[c])\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "origPopMAP = MAP   #origPopMAP is the map after eliminating coastal unpopulated tracts, with original islands\n",
    "print(\"Here is the revised state county map after eliminating coastal tracts\")\n",
    "plt.show()\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    print(\"Excellent!  We have no populated islands.  SKIP the below code block.\")\n",
    "else:\n",
    "    print(\"We appear to have some populated islands.  Separate out the main state geom in next block.\")\n",
    "LAT = MAP.centroid.y\n",
    "xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "print(\"we will scale longitudinal distance by\",r5(xScale))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8a562a21-4107-4a3c-a320-90442a473387",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\n",
      "unit 3739 is a true island.  We will move it to adjoin the mainland in same county.\n",
      "unit 3759 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 3068 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 3097 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 1006 is a true island.  We will move it to adjoin the mainland in same county.\n"
     ]
    },
    {
     "data": {
      "image/png": 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4HvAIBsLHISgoGAADjiNhwCEiot4nJxEIHtr2Mg2iCJz8AYidBygUCAkJAcAxOI6EAYeIiHofQQAEZdv797wBVGQBcQsAACEh7MFxNAw4RETU+2h1gKGNsJL8NbD5H8Al/wdETgQA+PtbBiIz4DgOBhwiIqImOYnAd0uBYX8BLvuHdbOnpycABhxHwoBDRES9T3km4B7Qcvuuf1luHb/qTctlrEZNAYdjcBwHAw4REfUuoghkHwTCxjTfXpIGpGwExt0DqDTNdnl4eABgD44jYcAhIqLepSQVqC4AIsaf3yaKwE8PA97hlstTf8KA43hUchdARETUo07+AKjdgOgp57f9/hqQvgNI+AbQuLV4SlPAqaio6KEiqbsYcIiIqHfJPQyEjQaqC4HDH1suSxUkA5MfAvrPaPUpTWNwCgoKerJS6gYGHCIi6l0MNcDZXcAboyy9Nf1nAVMfAwbNa/MpTT04eXn5PVUldRMDDhER9S7h44CcP4BJD1oGFGvc232KNeDk59m6OpIIAw4REfUuUx8DpvwNUHT8PhvrGJzychsVRVLjXVRERNT7dCLcAOfH4JDjYMAhIiJqR1MPjhQq9BXYmrkVDeYGyc5JLTHgEDmBYzkVKKsxyF0GkdOSIuCcKTuDXdm7cLToKKb0mYIt57ZIUBm1hQGHyMFV1DXg2rf3YOZ/d2F7SqHc5RA5JXf38wOR9Xp9p59/MP8g1Ao1poRNwSVhl0CtVGNU0CgkFyVLWSZdgAGHyMHtSCmEwWRG/0AP3LbuIJ78/hjqDCa5yyJyKkql0vp5R2czNotmfHP6G2zN3AqzaEaUV1Sz/QU1BQjxCJGyTLoA76IipyWKImoMJlTXG1HXYEKdwYS6BhPqGz+vNzZ+bLBsN5kta+sJaPooQKkQoFYpoFEKUCsV1odGJUCjVEKtFKBSChAEAUrBcryi8aNaKcBFrYSrWgmFQoDBaEaDyfIwGM0wmMxoMInNv7ZuN6PBKJ7/vPHYpnOYRVheW6HAtpRCxPXR4dMl4/DxvnN4/qeT+D21GK/dGI+4Pl5y/zMQOZ3q6mr4+fm1e9wf+X9gZtRM6DS6VvdXGCow1HWo1OVRIwYcsmuiKKKuwYSKugaU1zagou78o7LxY1vbK+oaYDSL7b6GWinARaWEUilAFC2vKQKACJhE0Rou5KBUCNAoFVApL/ioUkCAAKPJDKNZhMks4v8uHwhBEHDLhChM6OeHZV8k4eq3fsfDlw/A3VP6QakQ2n8xIuqQjvTgiKKIqoaqNsMN2R4DDtmU2WwJKDUGI2r1lo/V9UaU1TagrNaAsloDymsbUFpjQGVdA/RGszWkVNZbQkpb4cJdo4SXqxo6VzW8XNXwdlMjSOcBr8avvVzV8HLTwFOrgqvG0pPS9FGrVsBVrYSLWgm1sv0rtaIonu9xaexF0Tf2tpjNIkyiJWiIImAyizCLlt6WeqMZdQYTzKIIjVIBtUoBtVKAVnVhb5DCsq/xc3Vjb5FGqYCiC8EkJtAT65dOwqtbTuOfv6Rgx6ki/PuG4Qj3bbm+DhF1nEKhgNlsRlVVVbvHHio8hJGBIy96jFk0S1UatYIBh1plNouoN5pQa7Bcxqk1mFBrMKJGb0JJjR4l1QaU1hisn1fVG6E3Nh1nObbp87YIAuDlqoaPmwbebmp4aFVw0ygRpPOAzqUpoKibhRgvVzW8G7/uSDCRiiAI0KqU0KoAaHvsZbtMo1Lg0dmDMHVgIB76IglXvPYbnpk/BNfE94EgsDeHqCuCg0OQm5tz0R4co9mIpMIk1Jvq4ePi0+Zx5yrPIcgtyBZlUiMGHCdgNovQG82orG9AelENXDVK6BtMqNYbUVVvRFV9A6oaP6/VNwaPhqbgYrwgwFjGotQajKhvuPhfFlqVAv4eWvh5aODrroG/pxYapQLuWiXcNCq4a5Rw0/7po0YFd60S7loVfNw08HJV89KJjY2N9sXGZZfg6e+P4+Evj2DrqUK8cHUcvN00cpdG5HCCg4NbDTiiKOJ4yXGU1JVAqVBiRMAIeGguflv52YqzmBo+1YbVEgOOnTGaLJdoyq1jSyyXcMprLdsqag3Ir6xHamE1csvrYWq8FHIxKoUATxcVPFxUcNdYekksl2pUCPR0sX7t1hhCXNXKC45p3Na430Orgq+7Bm4aJXsCHITORY3//GUELosNxN+/PYbZr/6Gh2cOwFUjQuGiVrZ/AiICAISEBAM4PwYnvTwdWVVZAIAh/kMQ5x/X4XMpBf7s2RoDjsREUUR9Q+M4kvrzA14tnxtbjC+prDNaP6+oa0BVvbHV87qqlfBuvFwT4KnFJf0DEObjCpVCgLYxkJhFEafyq3BJTABCvF3g6aKCp1YNF7WCYYQwb1goRkX64Knvj+Ox9UexcuNJ/GVMBG6ZEIk+3q6tPqdp7JFWxV/G1LtVG6qhHqCGeq8aybXJ2Jm1E9Fe0bg0/NJOn6usvuyil69IGgw4Evj3ryn46WieNci0NShWo1RA56qGzlVlGVfiooa/pxb9At3h6WIZW+LjpoGXm+VzbzcNfNws4034lzZJIcTLFWsWjUZGcQ0+2ncOn+w/hzW70jBzcBBunRiFCX39rGHYbBbx4BdJ2HGqEEun9cPtk6L5fUi9Um1DLXbn7EawKRhKDyX8Kvy6FGyapJandqq3h7qGAUcC21MK4aJW4tqRfawDYnUuzYMMQwrZkyh/d/xj3mA8PHMAvj2cgw/2ZOCmtfsxMMgTiyZG4pr4PvjnLyn48Wgurhgagv/8ehof7T2H/7t8IK6J78OxU9RrmEUztmdtx5zoOdjpthP1mfWoqa7p1jnrjHVwVbXea0rSYcCRgAABIyK8cN9l/eUuhahT3LUq3Dw+EgnjIrA3rQTv78nAP747hhd+OolagwnPXR2HW8ZH4mxxDf75yyk88tURvLv7LJbN6I9gnUuL80X5u8PLVS1DS4ikd7LkJDKrMjEtfBoUgsK6HlVHZzJuTU1DDVQKvvX2BP5floAgAKI888ARSUIQBEyM8cfEGH9kldbi0wOZCPTU4pbxkQCAaH93vJ0wConnyrDy55O4+6PEVs+jUSkwc3AQrhsZhkv6+0PVg7fyE0nFLJrx89mfMTpoNGL9Yq3bmwJOR+bBaY3BZMCWc1twZb8rJamTLo4BRwKCIEBkwiEnEe7rhsdmD2p136hIH3x1zwSkFVXDYGz+PW8WRexJK8Y3iTm47f2D8PfQ4uoRoUgYH4lof/dWz0dkj/bl7cO08GlwVzf/vvX09AQAVFRUdOm8e3P34oq+V0AhMPj3BAYcCQhgDw71HoIgICbQs9V9cX28sOSSvjieW4lvDmXj28M5+OxAJtbeOhoT+/n3cKVEXWM0G1uEG+B8D05BQUGnz3mu8hx8XHygVvASbk9hjJSAIACNqxcR9XqCICCujxeeunIIfntsGkZF+WLxuoP49Xi+3KURtausvgw+2tZv4W4KOLm5eZ0+b0ZFBoYFDOtWbdQ5DDgSUAgCOrCmI1Gv46ZR4X+LRmNmbBCWfnIIXydmy10S0UWdKDnRbNzNhZoCTl7+xQNOg7kBP6X/hAN5B6zbeFmq5/H/uAR4iYqobRqVAq8vjMcNo8PwyFdH8N7us3KXRNQmk2hq8y6npjE41e0MMt6VtQuXR14OlUKFgpoCGM1G3jklA/4flwAvURFdnFIh4MVrhsLLVYNnfzyB8loDHpo5gDNsk11p72aRph6cizlXeQ5hnmFQK9UYGTQSv+f8jsNFhzE2eKxUZVIHMeBIQIAA5huiixMEAY/PGQRvNzVe2ngKFXUNeOrKIVBw0kCyE6nlqYjxjmlzf3sBR2/S41TpKcyKmmXdNqnPJMnqo85hwJGAIFhukSWi9t1zaT94uaqx4ttkVNQ14F/XD+d8OWQX8mryMCVsSpv7Lww4oii26IE8mH8Qk/tMtll91Dn8rSIBgR04RJ2ycGwE3lw4Ej8ezcOKb5M5jxTJriPfg+7u528d1+v1LfYP9huMsxUcY2Yv2IMjAQECBxkTddLcYSHQG014+MsjCPZyxcMzB8hdEvViR4qOYKj/0Iseo1Cc7xOorq6Gi0vz5UoyKjIwwIffx/aCAUcCCgUvURF1xbUjw5BfWY9XNqUgSKdFwrhIuUuiXqpcX44RLiM6fHxVVRX8/ZtPXllrrIWHpv2ByNQzGHAkIEDgJSqiLlp6aT8UVNTjH98dQ6CnC2YODpK7JOplMioy0MejT6ee09qCmwI4YN6ecAyOBAQBHIRD1EWCIODJK4dg1pBg3P/ZISSeK5O7JOplMqsy0d+nf7vHpaWlWT//+z+ebLGf04XYFwYcifAbm6jrlAoB//3LCAzr4407PziItKKWfx0T2UKDuaHDx+7YscP6+YED+21QDUmJAUcCCkGA2Sx3FUSOzUWtxNpFo+HvocWt7x1AYWW93CVRL3Aw72CHJ+EbMWIEAEDpGYAhgwe32O+h9kCV4eKzHFPPYcCRAGcyJpKGl5saH9w+FkaTiMXrDqKqvuN/XRN1ltFshMFsgIvKpf2DAQwZMgQKpRKmqiIEBgS02O/r4ouyel5itRcMOBLgWlRE0gn1dsX7t49BVlktln58CAYju0fJNrKrstHPu1+Hj3dxcUH/AYMAAAGtBJx6Uz20Sq1k9VH3MOBIQOBq4kSSGhSsw5pbRuPA2VK8+PNJucshJ5VekQ4/F79OPWfMqHgArQccvVHf4d4gsj0GHAlYltJhwiGS0oR+fnhsziC8vycDB86Wyl0OOSGdRge9qeWMxBfTNA7nz3PgAECFoQI6jU6K0kgCDDiS4EzGRLaweGIURkX64LFvjqLOYJK7HHIyo4JGYW/uXpjMHf/eago4rfXgAGixPhXJhwFHAlyLisg2lAoBLy8YhpzyOvxnc4rc5ZCTEQQB0yKmYWvm1g4/Z9y4cfjhhx8xbdq0FvvMIseL2RMGHAkouJo4kc3EBHrg4ZkD8O7usziUyTtUSFquKlf09+mPbZnbUFrf/qVQDw8PzJs3F35+zcfuHC85jiA3zsJtTxhwJMDFNols687J0RjaxwuPfn0U9Q28VEXSivaKxmURl+FQwaEuPf9A3gG4Kl0x0HegxJVRd3Qr4Lz00ksQBAHLli2zbluzZg2mTp0KnU4HQRBQXl7eoXPl5OTg5ptvhp+fH1xdXTF06FD88ccf1v2iKOLJJ59ESEgIXF1dMWPGDJw5c6Y75UuGl6iIbEulVOCV64bjXEkNXt9qHz/35HwG+w3G8eLjnXqOKIqoN9Wjr3dfG1VFXdXlgHPw4EGsXr0aw4YNa7a9trYWs2fPxooVKzp8rrKyMkyaNAlqtRobN27EiRMn8O9//xs+Pj7WY1555RW8/vrrWLVqFfbv3w93d3fMmjUL9fXyz3YqCJZvciKynYHBnnjgsv5YvSsdydkVcpdDTijUIxRl+s5dBj1RcgLRXtE2qoi6o0uriVdXVyMhIQFr167F888/32xfU2/OhWt2tOfll19GeHg41q1bZ90WHX3+G0YURbz66qt44oknMH/+fADAhx9+iKCgIHz33Xe48cYbu9IMyQgCL1ER9YR7pvbDpuP5+NvXR7DhvsnQqHiVnaQjiiIUnfi7P7koGSqFCuGe4TasirqqS78d7r33XsydOxczZsyQpIgNGzZg9OjRuP766xEYGIj4+HisXbvWuv/s2bPIz89v9npeXl4YN24c9u7dK0kN3WFZTJwJh8jW1EoFXrluGFILq/HW9lS5yyEnU1BbgEC3wA4fX6YvQ6xfrA0rou7odMD5/PPPcejQIaxcuVKyItLT0/HOO++gf//++OWXX7B06VI88MAD+OCDDwAA+fn5AICgoOYj1IOCgqz7/kyv16OysrLZw1bYg0PUc4aEeuGvU/vhre2pOJlnu59r6n0yKjMQqYvs0LF1xjpolBobV0Td0amAk5WVhQcffBCffPIJXFykm47abDZj5MiRePHFFxEfH4+77roLS5YswapVq7p8zpUrV8LLy8v6CA+3XRcibxMn6ln3XdYf/QI88Levj6DBxLlHSBpGsxFqpbpDx+7M2okxQWNsXBF1R6cCTmJiIgoLCzFy5EioVCqoVCrs3LkTr7/+OlQqFUymrt2+GRISgsF/Wno+NjYWmZmZAIDg4GAAQEFBQbNjCgoKrPv+bPny5aioqLA+srKyulRbR3CxTaKepVEp8M/rh+FEbiU+P5ApdznkJAR0bBbiPbl7MCZ4DJQKpY0rou7oVMCZPn06kpOTkZSUZH2MHj0aCQkJSEpKglLZtX/sSZMmISWl+Sylp0+fRmSkpaswOjoawcHB2Lr1/GyTlZWV2L9/PyZMmNDqObVaLXQ6XbOHrQiCwBE4RD1sWJg3ZscF48O953gXI0miI2Mpz1Weg4/WB36unVukk3pepwKOp6cn4uLimj3c3d3h5+eHuLg4AJbxMklJSUhNtQwAbApEpaXnZ4icPn063nzzTevXDz30EPbt24cXX3wRqamp+PTTT7FmzRrce++9AGCda+f555/Hhg0bkJycjEWLFiE0NBRXX311d/8fdBvX2iSSx83jInGmsJqLcVKPyazM5MBiByH5PZarVq1CfHw8lixZAgCYMmUK4uPjsWHDBusxaWlpKC4utn49ZswYfPvtt/jss88QFxeH5557Dq+++ioSEhKsxzz66KO4//77cdddd2HMmDGorq7Gpk2bJB0L1FWCIHAMDpEMJvTzQ19/d3yyn5epqGdwvSnHIYi9pG+3srISXl5eqKiokPxy1SNfHcHZ4hp8s3SipOclovb977d0vLzpFPYunw5/D63c5ZAD252zG5P7TG5zf1p5GjQKDcJ1nPemJ3X1/ZuzZEnAMsi4V+REIrtz3agwKAQBX/2RLXcp5MBSSlPanZE4pzqH4caBMOBIQBAAM/MNkSy83TSYNywUnx44BzN/EKmLcqtz0cejj9xlkIQYcCSg4F1URLJKGB+BrNI67DpTJHcp5IDMorndSfvSytO45pSDYcCRgMCJcIhkFR/ujdgQHT7ex8HG1HmiKEIQLj4HTnpFOteccjAMOJJgDw6RnARBwM3jI7DtVAFyy+vkLoccjFKhvOjdUQfyDiDOL64HKyIpMOBIgEs1EMlv/og+cFUr8e3hHLlLIQfU1izGx4uPw9fFFyEeIT1cEXUXA44EBIFXqIjk5qFVYUioF04XVMldCjkYg8nQ6rILBpMBJfUliPGJkaEq6i4GHAkI4GriRPagj48rcsp4iYo651TpKQzyGdRi+8H8gxgXMk6GikgKDDgSEHiJisguhPm4IodjcKiTyvXl8HbxbrZNb9KjwdwArZKTRzoqBhwJKNoZfU9EPaOPtyvyK+thMHI6feqenVk7LzqrMdk/BhyJsAOHSH59fFwhikB+Rb3cpZCD0Jv0zXppahpqsOXcFgz1HwqVQiVjZdRd/NeTgCAAIm8UJ5JdH29XAEB2eS0i/NxkroYcQU1DDTQKNxwrPobiumJ4qD1wadilUCvVcpdG3cQeHAkoBIFLNRDZgVBvV6iVApKzK+QuhRzEM9+dwfXvfg81vPHzfh9k5QUz3DgJBhwJcLFNIvvgolZiTlwIPj2QyXWp6KJEUcR3h3PwfVIxjJXxmPWvI/jyj2y8syNN7tJIIrxEJQHLJSoisgeLJkTiulW52HWmCFMHBspdDtmZcyU1+CYxG98m5SCr9Pwdd5f098e5klpE+PLSprNgwJGAIHAeHCJ7MSrSB7EhOny09xwDDlnllNfhtS2n8XViNtw1KlwxNATXXNcHY6N8oVBY7oSd98Zv8HW/+KKb5DgYcCRgmcmYCYfIHgiCgEUTIrHi22RkldYinH+R93rbUwpx90eJ8NSq8I95g7FwbARc1C1nLh4S4oW96SUwm0Vr6CHHxTE4EhC42CaRXZk/IhQeWhU+3n9O7lJIZnqjCc9sOI4xUT7Y+eg03DYputVwAwALRoUhs7QW+86W9HCVZAsMOBLgWlRE9sVNo8L1o8Lx5cEs1DeY5C6HZCKKIl7fegbZZXV4ct4QeGgvftFiTJQPInzd8HNyXg9VSLbEgCMBriZOZH9umRCJstoG/HiUb1a9UWmNAQ9+noS3tqdh2Yz+GBjs2e5zBEHAuGhf/JFR1gMVkq0x4EiAi20S2Z9of3dMGRCAj/ZmyF0K9TCzWcSdHxzErjNFeO3GEbjvsv4dfu4lAwJwKr8KZ4trbFgh9QQGHAlwKSoi+7RofCSOZFfgSFa53KVQD0rKLsehzHK8fmM85o/o06nnXj44CH7uGry3+6yNqqOewoAjAQG8REVkj6YNCkQfb1d8uJeDjXuTg2dL4a5RYlKMf6ef66JW4taJUfgqMQsl1XobVEc9hQFHApwHh8g+KRUCbh4fiR+O5qK0xiB3OdRD0otq0DfAA8ou3up9y/hICBDw8b5MiSujnsSAIwEutklkv/4yJhwA8OUfWTJXQj3BbBaRmFmGmECPLp/Dx12DucNC8MPRXAkro57GgCMBDjImsl++7hrMGxaCj/edg4nrUzm9H47mIrWwGgnjIrp1nhmxgUgtrEZeRV37B5NdYsCRgCCAq4kT2bErh4Uiu6wOueV8s3JmBqMZ//71NGbEBmJ0lG+3zjU4xAsAkJJfJUVpJAMGHAlYLvMy4RDZK42Kv+p6g19P5COztBb/d/nAbp8rzMcV/h5a7EsvlaAykgN/6iXAQcZE9o0/n73DN4nZiI/wRmyIrtvnUigETBngjx0phRJURnJgwJEIbxMnsl9NNwFwzirnVVhVj11nirFgZJhk55w6MBCn8quQVVor2Tmp5zDgSEAhcLFNInvW9PeHwITjtDYk5UIpCJg3LESyc86IDYS7RomvE7MlOyf1HAYcCXCxTSL71vTjyXjjvH48mofLBgXC200j2TndNCpcOTwUXydmw8w7SRwOA44EBFhWrSUi+9T088kOHOeVWVqLuD7dH3vzZ9ePDkdOeR32pJVIfm6yLQYcCbAHh8i+ne/BYcJxVraa42hkhDf6BbhzokgHxIAjAY7BIbJvQZ4uAIATeRUyV0K2MijYEwczyiQ/ryAIuGF0ODYdz0etwSj5+cl2GHAkwktURPYrNsQTQTotDp0rl7sUspGFYyOw83QRvrJBT0tcHy8YjGaUVHM9M0fCgCMBQRA4kzGRHRMEAa5qJeoaTHKXQjZydXwfzB0Wgr99fRTv7T4r6bkDPbUAgBzOhO1QGHAkoOBim0R2b1iYN/alc6CoM3vk8oHQKBV4edMp1BmkC7NR/u7QqhRIyiqX7Jxkeww4ErDcRSV3FUR0MTMGB+F4biWyyzhpm7OK9nfH5oenQG80Y+upAsnOq1YqMG1gIH46mifZOcn2GHAkIHCQMZHdmzYwAF6uary65YzcpZANRfq5Y1CwJ3amFEl63vkjQpGcU4G0ompJz0u2w4AjActt4ow4RPbM00WNJZdE4+fkPBiMZrnLIRuK9HNDboW042WmDQqEp1aFr/7grMaOggFHAlxsk8gxzBoSjFqDiVPvO7m8inqEebtJek4XtRK3TIjEe7+fRUp+laTnJttgwJGAAPASFZED6B/kiTlxwVj3+1n2ujops1lEbnk9vN3Vkp/7/sv6o6+/O+744CDSeanK7jHgSEAQuJo4kaO4cWwEzhRW40g2J/1zRr+eKEBxtR6XDw6S/NyuGiXWLhqN+gYTXtp4SvLzk7QYcCQggJeoiBzF5Bh/hHi5cOp9J1TfYMLTG47jskGBGBnhY5PXCPd1w1+nxmBHShEqahts8hokDQYcCSi4vA2Rw1AqBFw/KgzfH85BZT3foJxJUZUe+ZX1+MuYcAg2XFl13rAQGM1mbDzG28btGQOOBJp+jnhNn8gxJIyPhN5o5h0xTqaPtysCPLU4dE76NakuFKhzwaQYf7y/JwN6I2fHtlcMOBJoWqGYyzUQOYYgnQuuGBqCD/Zk2GwVaup5CoWASwcEYHtKoc1f6/E5g5BWVM15lewYA44E2IND5HhumxSFzNJabDtl+zdD6jnxEd5IK6qxeXAdEuqFZTMGYNXONC4BYqcYcCTQdK2X8YbIccRH+GBEuDfW/S7twowkr1AvV5jMIgqr6m3+Wvdc2g9jIn1x27qDOFPAuXHsDQOOBJqGsvFWcSLHctukKOxJK8Gp/Eq5SyGJhHi7AAByy20fcJQKAe/fPgZBOi2e/fGEzV+POocBRwLnL1HJWwcRdc4VQ0MQpNPigz0ZcpdCEgnzcYNCAE7m9UxoddOo8NjsQfjtTDGOZpf3yGtSxzDgSEBhw9sRich21EoFFk2IwjeHcpBfYfu/+Mn2PLQqTIrxxyf7M3tsAPnlQ4LRx9sV7zMo2xUGHAmwB4fIcd0yIRJuGiXe2p4qdykkkYdnDsDJvEp8uv9cj7yeUiHg1omR+PFIHoqq9D3ymtQ+BhwJcQwOkePRuahx95R++PxgJrJKa+UuhyQQH+GDhWMjsHLjKVT10GSOfxkdAaVCwGcHMnvk9ah9DDgSUPAuKiKHduvESHi5qvHGNs5p4iwemB6D+gYTfjzaM7MNe7mpcXV8H3y87xwaTOYeeU26OAYcCXAeHCLH5qZR4a9TY/DNoRyuEu0kQrxcERuis/msxhdaPDEKhVV6bDyW32OvSW1jwJEAZzImcnw3jYtAgIcWr21lL44zqG8w4VxJLaL83XvsNQcGe2JiPz+8z7mV7AIDjgSsN1Ex4BA5LBe1EvddFoMNR3KRks9J2xzd1pOFqNYbccXQkB593VsnRuFQZjlvGbcDDDgSaFpNXGTCIXJoN4wORx9vV7zw80mOo3Bw3x7OwfBwb0T3YA8OAMyIDeIt43aCAUcSjYOMmW+IHJpGpcCKK2Kx63QR3tzG28YdVVmNATtPF+KaEaE9/tq8Zdx+MOBIoOkSFW8TJ3J8VwwNwaQYPxzMKJW7FOqin5LzYBaBecN7PuAAllvGAeD7pBxZXp8sGHAkwCE4RM7l+lHh2JNW0mPT/ZO0vjucg0v6+8PfQyvL63u5qTF1YAB+6KFb1Kl1DDgSsM6Dw4RD5BTmDQtBH29XvL0jTe5SqJOySmvxx7kyXBPfR9Y6rhweiiNZ5cgs4eSRcmHAkQDnwSFyLiqlAn+d1g8/Hs3F8dwKucuhTvj1RAFc1ArMHBwkax3TYwPhqlbix+RcWevozRhwJGANOPKWQUQSumF0OPoFeODv3x7rsUUbqfvyyusQ6u0KN41K1jrcNCpMjw3ED0d4mUouDDgSEHiJisjpqJUKrLx2KJKyyvFJDy3aSN1XWmOAr5tG7jIAWC5TncyrxNniGrlL6ZUYcCRwfpAxEw6RMxkT5YubxkXglU0pyC2vk7sc6oDK+ga4a+XtvWkyOcYfaqWA384UyV1Kr8SAI4GmHhz2YhM5n8fnDIK7VokV3yZznJ0D6BfogVP59nH3m7tWhfgIH+w+Uyx3Kb0SA44ErD04/OVH5HR0Lmq8eM1Q7EgpwreHOa+JvRscokNBpd5uQs7kGH/sTS+BkTNj97huBZyXXnoJgiBg2bJl1m1r1qzB1KlTodPpIAgCysvL2z3P008/DUEQmj0GDRrU7JipU6e2OOaee+7pTvmS4W3iRM5temwQ5o8IxTM/nEBhVb3c5dBFDAjyBAAUVxlkrsRiUow/quqNOJrDu/F6WpcDzsGDB7F69WoMGzas2fba2lrMnj0bK1as6NT5hgwZgry8POtj9+7dLY5ZsmRJs2NeeeWVrpYvqfO3ictbBxHZzlNXDoFKIeCp74/LXQpdxGcHMuHvocHoKB+5SwEADA/zgqdWhd95marHdSngVFdXIyEhAWvXroWPT/NvomXLluHxxx/H+PHjO3VOlUqF4OBg68Pf37/FMW5ubs2O0el0XSlfchxkTOT8fN01eGb+EGw8lo+fk3nrr73afKIA18T3gYtaKXcpACxzKo3v54fdqQw4Pa1LAefee+/F3LlzMWPGDMkKOXPmDEJDQ9G3b18kJCQgMzOzxTGffPIJ/P39ERcXh+XLl6O2tu0ZIvV6PSorK5s9bIW3iRP1DnOHhmDWkCAs+yIJq3ZylmN7ozeakF9Zj5hAD7lLaWZyjD8OZZahqr5B7lJ6lU4HnM8//xyHDh3CypUrJSti3LhxeP/997Fp0ya88847OHv2LC655BJUVVVZj7npppvw8ccfY/v27Vi+fDk++ugj3HzzzW2ec+XKlfDy8rI+wsPDJav3zzjRH1HvIAgCXrxmKAxGM97iauN2p7BSD1EEgr1c5S6lmemxgWgwidh2qlDuUnqVTk0WkJWVhQcffBCbN2+Gi4uLZEXMmTPH+vmwYcMwbtw4REZG4ssvv8Qdd9wBALjrrrusxwwdOhQhISGYPn060tLS0K9fvxbnXL58OR5++GHr15WVlTYLOU2XqLiaOJHz8/PQYvUto3D3R4nYfKJA9iUB6Dy90XKnkpvGPi5PNQnzccPwcG/8nJyH+SPkXSOrN+lUD05iYiIKCwsxcuRIqFQqqFQq7Ny5E6+//jpUKhVMJpMkRXl7e2PAgAFITW37L6Rx48YBQJvHaLVa6HS6Zg9b4SUqot5lZmwQZg4Owr2fHMJnB1peTid5uKgtb2k1eqPMlbR0RVwwdqQU2WVtzqpTAWf69OlITk5GUlKS9TF69GgkJCQgKSkJSqU0qbm6uhppaWkICQlp85ikpCQAuOgxPUXR1IXDi1REvYJCIeDNm+Jx1YhQPLXhOPIreOu4PcgstYzL9PfQylxJS1cMDYHeaLaLy1SphdWoqHP+8UCdCjienp6Ii4tr9nB3d4efnx/i4uIAAPn5+UhKSrL2rDQFotLSUut5pk+fjjfffNP69SOPPIKdO3ciIyMDe/bswTXXXAOlUomFCxcCANLS0vDcc88hMTERGRkZ2LBhAxYtWoQpU6a0uE1dDk1jcDiTMVHvoVUp8dSVg+GmUeLN7WfkLocAfLzvHCL93BAbYh932F4o3NcNQ/t4YeMx+e/Am/GfnRj+zK/4z+bTTh3OJV+wY9WqVXjmmWesX0+ZMgUAsG7dOixevBiAJbAUF5+/ZS47OxsLFy5ESUkJAgICMHnyZOzbtw8BAQEAAI1Ggy1btuDVV19FTU0NwsPDsWDBAjzxxBNSl99FvERF1Bt5uqhxz6X98O9fU3D3lH4I93WTu6ReK62oGhuP5eOFq4dCeb5b3a5cMTQEr209jeJqvU17meoMJmw7VYhvDmVjf3oJfD008HbVYHZcMKbHBlqPe/e3dLy1PRV/GROOp68cAo3KuRY3EMResr5AZWUlvLy8UFFRIfl4nEOZZbj27T3YtOwSDAq2v78ciMh2ag1GTHllB2JDPLF20Wi7mX+lNxFFEXd+8AeO5VZg16PToFXZ579BaY0Bl/17B6YPCsK/bxhuk9c4lFmGuz9KRFGVHoOCPTEjNghGs4jUwmpsOVkAQbD8MX7ftBjcfWlffHEwCy9vOoXxff3w/NVxCPFytbug09X3b/tYctXBcakGot7LTaPCK9cNxdKPD+H+zw7j7YSRUCvt6w3C2X17OAdbTxVi7aLRdhtuAMtkkY/PHoTH1yfj+tFhGN/XT9LzV9Q24P++PIJQLxd8efcERPu7N9tfrTfigz0Z2JdeglFRPvB0UePOS/picIgOd32UiEv/uQNermq8vGAYBgV7IsLXDQo77Q3rCPbgSOBIVjnmv/U7fnpgMoaEekl6biJyDNtOFeCuDxPx16n98PDlA+Uup9eobzBh3ItbMW1gAF69MV7uctplNou4btUeVNYb8fMDl0jSW2I2i/g6MRsvbzoFg9GMb++d1OnJDgsq63G6oArv/56BrY0DoYeE6jAywgfuWhUenN4fCgWw+0wx/Dy0GB7mZb2D2NbYgyMjrkVFRJcNCsI9l/bD6l3puGFMOMJ8OB6nJ5wuqEJFXQMWTYySu5QOUSgEvHDNUMx7Yzf+tzsdf50a063zHckqx5MbjuNIVjmuie+D5XMGIVDX+XnqgnQuCNK5ID7CB0s/TsTAIE+kF9dgb3oJzpXUYHdqEWr0JpwtrgEAXDk8FP+6fphd95gx4EhA0UMplojs29Kp/fDlH1l48vvjWHPLKKh4qcrmghvfzAsr9TJX0nGxITrcPikKr289gyuHhXZpcHpJtR7//CUFX/yRhUHBOnx59wSMjfbtdm0eWhU+umNcs207UgqxYn0yhoZ54bUbR+BscQ3+9vVRAMAbC+2314w/fRLiTMZEvZu7VoWXFgzFztNFeHULbx3vCQGeWrioFcgqbXttQnu0bMYA+LhpsOLbZJg6OcfI/vQSTPvXDvycnIdnrxqCH+6bJEm4acvUgYHYs3w6Vt8yGsPCvDF/RB+svGYofjiSi+TsCpu9bncx4EiAl6iIqMllg4Lw0Iz+eGtHKvakcQVpW0strEZ9gxlRfxpQa+8sYXgYfk8t7tTCrSdyK/GXNfsQ5uOG7Y9MxS0TomTpKbw6vg/CfV2x9rf0Hn/tjmLAkYDQNA+OzHUQkX1YOjUG46P9sOzzJGQ0jlkg2/jxaB48tSpMGeAvdymddumAANw6MQqrdqahuFoPczs9OelF1bji9d8AAK8vHAE/GWdsVioEXBsfht2p9hviOQZHAorGmNhLbkgjB5GTk4PHHnsMGzduRG1tLWJiYrBu3TqMHj0aALB+/XqsWrUKiYmJKC0txeHDhzFixAh5i3YSSoWA124cgRvX7sO17+zBe4vHYES4t9xlOR1RFPFTch5mDgmy68GuF3PPpf3wyf5MjH5+CxQC4O2mgY+bGr7uGni5qlGtN6KwSo/0ohq4Ns6xdOXwUMQEespcORAT6IHSGgNKawzwddfIXU4LDDgSaOrB4VINZC/KysowadIkTJs2DRs3bkRAQADOnDkDHx8f6zE1NTWYPHkybrjhBixZskTGap1ToM4F39wzEYveO4DrV+3B5ocudbjLKPYupaAKqYXVWHHFILlL6bIgnQu+v3cSTuVXos5gRlmtJTCU1RhQVmuAv4cWvu4apBfV4IbRYVg6NQaBnvax1lZ8hDcA4MDZUsyOC5a3mFYw4EhA4GKbZGdefvllhIeHY926ddZt0dHRzY655ZZbAAAZGRk9WVqv4uOuwQvXxOGqN39HbkUdA47EfjqaBy9XNSbHBMhdSrfEhujscv2s9oT5uGFAkAe+O5xjlwGHY3Ak0JRveIWK7MWGDRswevRoXH/99QgMDER8fDzWrl0rd1m9kperWu4SnJIoivjpaB5mDQmyu6UFepNbJ0bh1xP5OJJVLncpLfC7QgLWu6jkLYPIKj09He+88w769++PX375BUuXLsUDDzyADz74QO7Sep2mtalO5FbKXIlz2XqyEOnFNbg6vo/cpfRqN4wOx5BQLzz0ZRLqDCa5y2mGAUcSnOiP7IvZbMbIkSPx4osvIj4+HnfddReWLFmCVatWyV1arxOkc8GiCZF4edMpHMosk7scp2A0mfHSplOYFOOHCRKv50Sdo1Yq8N+/DEdOWR3e3pEqdznNMOBIiJeoyF6EhIRg8ODBzbbFxsYiMzNTpop6tyfmDsawMG8s/diyyjN1z5d/ZCO1sBrL58T22HpI1LaYQE9cOTwUW08Wyl1KMww4Ejg/0R8TDtmHSZMmISUlpdm206dPIzIyUqaKejeNSoG3E0bCLAL3fXoIRpNZ7pIcVo3eiP9uOY2rR4Qirg8XN7YXg4I9kV5cLXcZzTDgSMA6yFjWKojOe+ihh7Bv3z68+OKLSE1Nxaeffoo1a9bg3nvvtR5TWlqKpKQknDhxAgCQkpKCpKQk5Ofny1W2UwvSueCtm0Yi8VwZXtp4Su5yHNZnBzJRUduA/+OK7Xalst4InYt9DahnwJFAUxcpO3DIXowZMwbffvstPvvsM8TFxeG5557Dq6++ioSEBOsxGzZsQHx8PObOnQsAuPHGGxEfH89xOjY0NtoXK66Ixf92n8UPR3LlLsch/ZSch8sGBXZpgUqyndTCKvQNsK9pEDgPjgR4BZjs0bx58zBv3rw29y9evBiLFy/uuYIIAHDbpCgcyS7HY98cRb8ADwwOdbz5T+SUW16HKf0de94bZ3QitxKXDQqSu4xm2IMjIZEXqYioHYIgYOW1Q9E3wB23vLsfpwuq5C7JYZTVGFBcbUCwl4vcpdCflFQbEKSzjxmWmzDgSEDgIBwi6gQ3jQof3T4OgToX3Py//civqJe7JIewamcatCoFZg62r54CAvw8NCiutq87BBlwJMDVxImos3zcNfjg9jFQKQTc+eFB1BqMcpdk13LK67BuTwaWXNIX/jKuok2ti/BzR0ZJrdxlNMOAIwFOw0BEXRHo6YL/3ToG6UU1ePiLIzBzxd42/efX09C5qLBkSl+5S6FWRPu54WxxjdxlNMOAIyHeRUVEnTU4VIfXb4zHLyfy8e/NKe0/oRc6mVeJ9Yez8cD0/vDQ8t4YexTt747MklqY7CikM+BIiIOMiagrZgwOwt9mDcTbO9KQeI7LOfzZK5tOIdLXDQvHRshdCrUh0t8dBpMZ+ZX2M56MAUcC52cylrcOInJcd0/ph+Fh3nj06yOorG+Quxy7sTetBNtTivC3WYOgVvIty175uWsAWO50sxf8bpGAdaI/mesgIselVAj41/XDUFilx5IP/kB9g32tzCwHs1nEiz+fxPBwb1wxNFjucugivF0tAaeyzn7COQOOBDjGmIikEBPoifcWj8GR7HI88NlhuxrPIIfvknKQnFOBJ+ZyUU175+NuWaahyI5uFWfAkQAX2yQiqYyJ8sXbCSOx5WQBXvml965ZVWcw4Z+/pGBOXDDGRPnKXQ61w9NFDU+tCjnldXKXYsWAIyHGGyKSwmWDgvD3uYOxemc6vknMlrscWbzyyymUVBvw+JxBcpdCHVCjN6JKb0SQp/3MMs377STQNNEfEw4RSeX2SVE4nV+F5euTEeXvhlGRvacXY+vJAqz7PQNPXTkYkX72tYAjte5UvmXJkUEhnjJXch57cCRgvUTFhENEEhEEAc9dHYcREd5Y9O4BJGWVy11Sj8gtr8MjXx3BjNhALJ4YJXc51EEn8iqhUgiICfSQuxQrBhwJcOgbEdmCRqXAusVjMCDYE/d8lIjCKvuZY8QWzhbX4IbVe+GmUeGV64ZzYLEDOZFbiZhAD2hVSrlLsWLAkQLnwSEiG3HXqrDq5lEwiSJuf/+g086RU1hVjxtW74VWpcCX90yAb+O8KuQYTuRVYnCoTu4ymmHAkRADDhHZQpDOBR/cNhbnSmpxpxPOkWMyi3jwsyQAwGd3jUcfb1d5C6JOMZrMOJVXicEhDDhOh6uJE5GtDQ7V4f3bxiA5uwJ/+/qoU4Wctb+lY//ZErx24wgE2tFdONQxGSU10BvN7MFxRpwHh4h6wqhIX6y8dih+PJqLyS9vw4ncSrlL6rac8jq8tuUMbpsUjYn9/OUuh7pgb3oplAoBQ0K95C6lGQYcCXAYHBH1lKvj+2D7/02Fj5sGD35+GJkltXKX1GWiKOLZH45D56rCshn95S6HuujX4/kY39cXXq5quUtphgFHAlyLioh6UpS/O95OGAmDyYzL/r0DUY//hLs+/AMVdrQOUEe8sS0VvxwvwJPzhsDTxb7eHKljymsN2JtWgllD7G+tMAYcCfEKFRH1lP5Bnvjh/sm4fnQ4AODXEwW48o3dqNEbZa6sY97dfRb/2Xwaj1w+AHOHhchdDnXRR3vPQaEQMCfO/v4NGXAkcP4SFRMOEfUcnYsaK68dioyX5uLnBy5BYVU9pv1rB17dchqFlfY7Z87nBzLx3I8ncM+l/XDvtBi5y6EuWn8oG29uT8XN4yIR4KmVu5wWGHAkIHAeHCKS2eBQHX68fzJmDA7C6p3pmPjSNtz36SEcOFtqNzdA1BqMePL7Y3h8fTIWTYjEY7MHcjI/B3U8twIPf3kEc4eF4NHZA+Uup1Vci0oCAocZE5EdiAn0xIvXDMVjswfhm8RsfLTvHG5YvRexITr887phiOsjz10uoijilV9S8NmBTNQ3mPDMVUNwy/hIhhsH9vG+TIR6ueCVBcOgUtpnX4l9VuVouNYmEdkRL1c1bp8cja0PX4oPbx8LpQJY8M4e/POXUz0+f87hzDIs/fgQ3tmRhhAvV/z8wCW4dWIUFAqGG0cliiK2nizA3GEhdhtuAPbgSMpOeoGJiAAACoWAKQMCMCbKF29tT8Wa39LxfVIuZsQGYeHYCAwMtt3Kz2U1Bvzj+2P48Wge+ga4Y8UVg3D7pGi7fkOkjsmrqEdhlR6jo+x7hXsGHAlwNXEismeuGiUemTUQc4YGY+7ru/H+ngxsOVmAH++fDC9XteSXirJKa/HSplPYmVKEVxYMw4JRYVCyx8ZpHGlc2T4+3FvWOtrDgCOBph9b9uAQkT0bEuqF5Kcvx9++OopNx/Mx4tnNcNco4eehxYS+fnjx2qFdCiINJjO+PZSDgxmlKK7WY3tKETRKBf42ayBuGBNug5aQnJJzKhCk0yJQZ9/LajDgSIAD5YjIUXi6qPHmTfE4nluJnPI65JTV4aVNp/DFH1l49uohUCqUHTpPncGEn5LzUF3fgF+OF2D/2RJE+bmjpMaA56+Ow/wRoZy8z0n5umtQXtuABpMZaju+5MiAIwFrD46sVRARdYxKqcDwcG8Mb7zEkFFSg28P57R7R2iN3ohDmWX46WgevkrMhlkUoVYqoHNR47Ml4zGur18PVE9yG9rHC3qjGelFNTYdx9VdDDgSspe5JoiIOuPGMRH4ZH8mBjyxEatvGYXLBgWioq4BhZV65JTX4cDZEhw4W4pjuZUwmUX4uWtw9Yg+uO+yGET5uUEUwbuiepFBwZZVw0/lVzLgODteoSIiRxbXR4cbRofhyz+ycfdHiS32h3i5YFy0L/4yJgJjo33RL8C92aV5/g7sXbzc1AjWueB0QZXcpVwUA44EONEfETkyQRDwynXDce+0GBzKLMMLP52Ei1qJRy4fiNFRPujj7cqxhk6gRm9EjcGIAA9tt/89fd01qKq373XPGHAkwKUaiMgZRPq5I9LPHdfEh8ldCtnAX9bsxbGcStw1pS9WXBHbrXM5wtud/Q5/dkCcB4eIiOxRjd6IYzmVAIA1u9K7fT6zWYTCznv12IMjIfbgEBGRvcivqMeWkwXYeboIm08UNNsX9fhPOPXcbLioOzYtwJ9V643wdLHvCGHf1TkIXqIiIiJ7YjKLGL9y60WPGfvCFuxZPh0e2s5Hgcr6Bgac3oCDjImIyJ4k51RcdP+cuGBsPJaPjcl5uH5089mmRVFEUZUeR7MroFQKMJpEeLmqUVpjQHZZLfRGM6r1Rnho7XsiRwYcCQhcTZyIiOxEQWU9HvjssPXrsVG+OJBRip8emIwoP3e4aZQQBAFRj/+EX44XoKhajzqDCe/tPouhYV5Iya9CWW1Dm+f3c9cgzMcVcX10PdGcLmPAkcD5tagYcYiISF5vbktFZX0DZsQGItrfHSuuiG31tvDLBgXieG4F/jhXivLGQHMqvwq3TYzGwGBP3PNx8zmRfN01mBkbhJevG9Yj7eguBhwJMd4QEZGcDpwtxXeHc7BwXES7t4K/t3iM9fOSaj1GPb8FoV6ueHBG/1aPL60xYHdqMURRdIh5kRhwJCDwGhUREcnsWE4Fbn53P0ZGeOPeqTGdeq6fhxYAcCKv0rrtsyXjUVhVj/kj+uCjvRn4x/fHkVNeh3MltYjyd5e0dlvgPDgSsP8cS0REzsxsFvHIV0fQL8ADH9w+Fl5u3R8APKGfH+aP6AMAWDDq/OSPu1OLu33unsCAI4HzHTjswiEiop6372wJTuVX4cl5g6FVdW1uG5/GUFRraLkEg5tGhQmNq8XvSXOMgMNLVBJoukTFMcZERL1bVmkt9qWXIKOkBm9tTwMAbFp2iXUF7j8ySvHpgUwYTSJUSgGzhgRj1pDgbr/u5wey0DfAHeP7+nb5HNfEh+G9388i8VwZLukf0GK/i9rSJ/J7aglMZhFKO19BngFHQsw3RES9V2FlPea9sRsVdc1vsZ796m849I+ZaDCZsXjdQXhoVYjyd0NlnRHrD+XgvmkxWDwpCqcLqvD8jydxuqAKggBcNyocz18d126QKKsxYNOxfDwya0C3Bv9eOTwE7/1+Ft8ezmk14JTXNSAm0AOphdVIPFeGsdFdD1M9gQFHQuzBISLqnc4W12Dav3YAAPYtnw5fdw0Wrt2HxHNlAIDiaj2e+eE4XNQK/LJsCrzc1BBFEW9uS8V/t5zGm9tTAQBjo33x5JWDUVJtwBvbzsBgNONf1w+7aHD59nAORIhYMLJ7i6TGhuis5/vPDSNa7K+oa8C0gYGorGvApmP5DDi9hQPcMUdERDawJ60Yi9cdhCBYZggO0mkhCAK+WToRpwuqcO3be3D5f3fBXaPE2kWjrQOABUHA/dP746ZxEdidWgxBEHBFXDBUSsuloCh/Nzz0xRFMGxSAecNCW33t0hoD/vdbOi4fEmy9E6qrmtalauuP9YraBvi4qTFrSDB+OZ6Pf8xrfX4de8GAIxEBHGRMRNTbnMqvxN0fJmJslC9W3zIK7n9a12lAkCd+fuAS/HoiH5cOCED/IM8W5/Dz0FrvVrrQNfFhWH8oB+t+z2g14JjMIh747DD0RjP+3s6cN5315zE2oiiioq4BXm4axIbo8NG+c3Z/uzjvopKIIAi8REVE1IvklNfh1vcOIMLPDataCTdNIvzccOclfVsNN+25cngoDmWWoahK32Lfv35NwZ60YryxMB6h3q6dPndrBgVbajx5wXw4AFBjMMFotqxJNTbaF0qFYPe3izPgSIj5hoiod6iobcDi9w5ArVRg3W1jurQid0dMHxQIAcDWkwWoaBz7snLjSazdlY53dqThsdmDMDHGX5LX+s/m0ziVXwUAuPODP5BaWGXdV9wYsHzdNPB0UWN4mJfd3y7erYDz0ksvQRAELFu2zLptzZo1mDp1KnQ6HQRBQHl5ebvnefrppyEIQrPHoEGDmh1TX1+Pe++9F35+fvDw8MCCBQtQUFDQnfIlJQAcZUxE1Es8ueEYiqr1+OD2sQj0dLHZ6/h5aDGhnx+e/+kk4p/9Ffd8nIjVO9Pxws8nER/hjbum9JXkdX44kovXt57BA9P747dHp0GjUuD1ranW/cdzLT06g0IsPTyTYvyxN60EZrP9vu91OeAcPHgQq1evxrBhzRfdqq2txezZs7FixYpOnW/IkCHIy8uzPnbv3t1s/0MPPYQffvgBX331FXbu3Inc3Fxce+21XS1fcnY8zqpdZrOIOoNJ7jKIiGxGFEXsSS3G90k53f59l5JfhQ1HcvG3WQPRL8BDogrb9vDMgbh8SBBevGYofnt0GvavmI6rhodi2Yzu3RbexGQW8c9fUjBzcBAemtEf4b5uiA3xRH5lvfWY7LJaaFUK+LhpAFgCTlltQ7OlHexNl/rUqqurkZCQgLVr1+L5559vtq+pN2fHjh2dK0SlQnBw65MdVVRU4N1338Wnn36Kyy67DACwbt06xMbGYt++fRg/fnyn2yA1AYKkl6jMZhEGk9nyMJpRZzChvsGEugYT6gyWj+e/Np//uvE4vdGM+gYT6ps+XrCtzmBCbeM5ag1G1DeYAQBPzhuM2ydHS9gKIiL5pRVV4x/fHcOetBIAgLebGosmRGHxxCj4ums6fb6fk/Pg5arGDaPDpS61VaMifTAq0qfZttcXxkt2/p+T85BZWos3b4qHIAioM5iwL70UiyZEWo8ZGekDvdGMvWklmNzfHyXVBgBAVX3LWY/tRZcCzr333ou5c+dixowZLQJOV505cwahoaFwcXHBhAkTsHLlSkRERAAAEhMT0dDQgBkzZliPHzRoECIiIrB37167CDgQgF+PFyCjuBYNJjMaGoNJU0BpaAwrDUaxlW0XHmfZb+pEt58gAK5qJVzUSriqldCqFdCqlHBRK+DS+NFDq4K/h+VzV7UKbholXDWW4900Svx78+lmaZ2IyBmIooiHv0hCaa0B/1s0Gv2DPPC/387ire2peHt7KmbEBuGf1w+Dp0vH1246kl2O+HBvqJWOP4zVbBbxxrYzmDIgAMPCvAEAvxzPR0VdA667YP2p0ZE+8HFT40BGKSb398eHezMwsZ9ft2ZOtrVOB5zPP/8chw4dwsGDByUrYty4cXj//fcxcOBA5OXl4ZlnnsEll1yCY8eOwdPTE/n5+dBoNPD29m72vKCgIOTn57d6Tr1eD73+/KjzykrbdqNN6ueH9OIaFFTWQ61UQK1SQKtUQK0SoFEqoFYq4KZVWbYpFdCoFI3HCdZtapXCcuwFz1UrLduawsifP7qoldCqFN3upvzf7rOdClVERPbMbBaRWlSNjcn5OJJdgfdvG4OpAwMBAM9dHYdlM/rjhyO5ePqHE5gY44dFE6I6dF5RFHEkqxy3TuzY8fYsv6Ier209jdMF1Vh57VDr9sOZZegb4I5Iv/O3gAuCgNFRvtiTWoyHZw6AySwizMfNeebBycrKwoMPPojNmzfDxUW6QVVz5syxfj5s2DCMGzcOkZGR+PLLL3HHHXd06ZwrV67EM888I1WJ7Vp329geey1bUAiAmYOkicgJ1DeYcOcHfzROngfcNaWvNdw08fPQYvGkaKzamY4nvz8OtVKBhWMj2j13ZmktymobMDzc20bV94xqvRE3rtmLiroGPDp7IEZFnu+JOZlXhSGhXi2eM39EKO779DC2pxTCw0WFan1Di2PsSaf61xITE1FYWIiRI0dCpVJBpVJh586deP3116FSqWAySTNQ1dvbGwMGDEBqqmUEd3BwMAwGQ4s7sgoKCtoct7N8+XJUVFRYH1lZWZLU5qwUnMeHiJzESxtP4UBGKVbdPBJ//H0GVlxkEjxPF8vf+cvXJyOrtLbdcydllQMARjReznFUH+zJQG55Pb796yT8dWpMs31ZZbWI9HVr8Zwr4kIwdWAAHvnyCEQRqNHb980pnQo406dPR3JyMpKSkqyP0aNHIyEhAUlJSVAqu7ZE+59VV1cjLS0NISEhAIBRo0ZBrVZj69at1mNSUlKQmZmJCRMmtHoOrVYLnU7X7EFtEwSBPThE5PC2pxTi/T0ZeGJuLGbHhbS7fEFTwAGA/2453e75j2RVINLPDT5dGJxsL0RRxGcHMnF1fGiLmYj1RhPyK+sR7tty4kCFQsDLC4ahqt6InaeLUFVv3z04nbpE5enpibi4uGbb3N3d4efnZ92en5+P/Px8a+9LcnIyPD09ERERAV9fSxfY9OnTcc011+C+++4DADzyyCO48sorERkZidzcXDz11FNQKpVYuHAhAMDLywt33HEHHn74Yfj6+kKn0+H+++/HhAkT7GOAsRPgJSoicnRGkxnP/XgCk2L8cMv4yPafADQbXHw4sxyFVfUXndcmKasMwx289ybxXBmyy+pwbSuLc+aV10MUgXCflj04ABCkc8H1o8Pwyf5MpBRUQRRFux2HI/kQ8FWrViE+Ph5LliwBAEyZMgXx8fHYsGGD9Zi0tDQUF5+fATE7OxsLFy7EwIEDccMNN8DPzw/79u1DQMD55dr/+9//Yt68eViwYAGmTJmC4OBgrF+/Xuryey2lQgDHGBORI1t/OAdni2uwfE7HF4H0uKAH52xxDca+sBWJ50pbPbawsh6HMssxLKzl+BRHsutMMXzdNRgb1fIOqKwyy2W6sDYCDgDcPaUfAKC+wYyc8jrbFCmBbs8t/ef5bp5++mk8/fTTF31ORkZGs68///zzdl/HxcUFb731Ft56661OVkgdYVlLiwmHiBzXJ/vOYeqAAMT16XgA0apa/p3/7A8n8P19k1tsP5BhCT5zhoZ0vUg7cKagCoOCPaFQtAyBWaV1UAhAiHfbvVgRfufDT2ZJ7UXDkJwc/yZ+koRCAMxmuasgIuqaYzkVOJJd0aE7oS60oJXLNEeyK3C2uKbF9hO5lQjWuaCPRAtbyiWvoh5hPq23IbusFiFeru3O8TMgyDKDc7Yd9+Aw4BAAy11UJvbgEJED+uV4Pu7/7DAifN1w2aDA9p9wgUPnylrdviEpt8W2t3ekOcWEqKU1Bvi6tz74Oqusrs3wc6H+gZY1qbLLGHDIznGQMRE5olU703D3R4kI9NTiw9vHQtXJ2YVdNZa7f/0uuCtKpRDwfVJOq5fto/zs83JMZ5RU6+Hv0fpdYHnldR3qoQprvMsqhwGH7J3AeXCIyMEczS7Hy5tO4b5pMfji7gktbnnuCA+tZShqRV0DYgItl10mNM5Mv/N0UbNjI3zdHH78TZ3BhBqDCX5tBJyKugZ4u7V/C7y7xvL/Lbus/bmD5MKAQwDYg0NEjsVsFvHk98cxMMgTy2b07/J5mu6iMppFa69GlJ87RoR7441tqdZenKIqPTJLa1sdlOxIDCbLYMu2ft1X1DU0mxuoLXqjZZI/e76LyrH/pUgyvE2ciBzF/vQSLHrvAI5kl+OZq4Z0+rLUhZp6cABA1zgnTn5lPR6YHoPEc2XYl265c2rbqQIA58eeOCovVzX6BrjjaHZFi32J50pRWKVHbEj7E+OW1Vom+curqLfbdQwZcAiAZZAxe3CIyJ5V1TdgyYd/4C9r9iG/sh7vLR6DcX39unXOC3srdK6WgFNQWY9pAwMxOESHVTvTAAACLLdUz4lrfXkgR9I/0AOphdXNttU3mPDCTycxIMgDlw8Oavcc5bUGAIDJLKKuwT6XbOj2PDjkHDgPDhHZs4LKeixedxDZZbV486Z4zB0aIskMuu6t9OCcLaqBIAi4bVIU/vb1UZwrqUGNwQhlK/PGOKJofw/8ePT8XWKiKOL+zw7jcFY51t4yutX5cf6stMYAD60K1XojavTGZj1h9oI9OASA8+AQkX0yGM3YdqoA1769B+W1Bnx9z0TMGxYq2fIAF74xN/XmVOmNaDCZceXwUHi7qbFmVzre35OBS/r7d+jN394F6bQoqtJb/6j9ZH8mNp8owNs3jcSMDvTeAEBZTQNiQyyX6/Iq7PPWefuLXCQLzoNDRPbkRG4l/vdbOjafLEBVvRHDw7yw6pZRCPGSdpI9T+35taiaLlEBQG55HSL93HHD6HCs2ZUOAFi3eIykry2XAE8t9EYzymob8MORXDz74wksmhDZqTvEymoNGNfXFwczylBop3MDMeAQAEsPDi9REVFPMJlFKATLpfHc8jrsPF2ESwcEILRx/pVP92fiie+SEertijsmR+PywcGIDfG0yaKO7lrl+c815z/PLK1FpJ87EsZFWANOe7P7OoqBQZael/Ert8JgNOPm8RF4ct7gTp2joq7BOl9Otd4oeY1SYMAhAJZfNPY6Ep6IOk4URRw4W4qUgipcMTQE/h6tz1jbGWU1BlTVG5FVVovjuRUI9HTBwGBPDAw6v57R76nFOJxZhuScCpTXNuClBcMQfcG8NGaziHV7MvD+nrPILqtDTIAHhoV54+fkPNQ1mKBzUeGV64Zh/9lSrPs9A4smROLJeYO7dYdUR6iUCriqlahrMEFvNMPfQ4viaj3yyi29EpF+7hgV6YPEc2XNengcWf8gT6y+ZRSSssoxd2hIp9buAizfYwaTGZ4uamiUClTVM+CQHVMKAhpEDsIhcmTpRdV4fH0yDpy13Nr81vZULJoQBcASMEZH+SI+whsuauVFzmJRozfi68RsfLo/EykFVdbtLmoF6hssvyuCdS6I8HVDvdGEo9kV8HXXoK+/O3LL67Dovf348u4JCPFyRUVdA5avP4qNx/Jx3cgwjLjUG3vTSnC6oAqLJkbi1glRWPZFEu75+BBc1Uo8MTcWd0yOtkmPTWvctSrUNZhQrTciys8NxdX6ZksyfHTHWJTXNsDLSQIOAMwaEoxZQ7p2R5jRLEIUAbVSgEIBNJjs872DAYcAAAoFOA8OkQPbdqoASz8+hGAvF6xbPAYxgR5Y8W0y3tyWCq1aAbNZxL83n4ZWpcCljStuzxwchEHBzS/9lNYYsP5QNlbtTENZbQMuHxyEey+LgZ+7BoGeWvQL8ECV3ojjuRXYcqIQ5XWW24XvmByNq4ZbBv9ml9ViwTt7cMnL2+HhokJ1vRFqpQKrbh5lfVNNGBfZrP5P7hyHxHNlGBjkCR/39mfSlZKniwrF1XrU6I0I8XYFzpU1GzjrplHBTcO3yyYqhQAXtQIbjuSivsGMwR2YN0cO/BcjALxNnMiR5ZTX4f5PD+OS/gF4Y2G8dX2lj+4YZz3GbBZxMr8SO08X4aejediXXoL/bD6NSD83LJvRH24aFb47nIMtJy0T2l01vA8emtkfYT4t117yclVjYj9/TOzn32o9YT5u2PTgFPx8LA+VdUZ4aJWYNSQYgTqXNtugViowvptz2nRV051UtQYTIhrXWCqw04Gz9kAQBOhc1PjtTDGG9vHChH7y/Lu1hwGHAHCiPyJH9ua2M3DVKPHfvwy3hps/UygEDAn1wpBQL/x1agwMRjP2pBXjo73n8NAXRwAAg4I98ficWFw9IhR+3Ry74+OuadFLY6+aAk613ogBjQNwM4pr5CzJ7kX7u6OwSo+V1w7tsUuJncWAQwA4Dw6Ro8otr8PXidl45PKB8HTp+BgRjUqBqQMDMXVgIM4W10ClEBDm42q3b1a21LQeVa3BiPDGHpx0BpyLen1hPPall2BIqH1engIYcKgR58EhckxrdqXDXatCwviu95ZEd2EVbmfiae3BMSE2RIcBQR6YOjBQ5qrsW5DOBfNH9JG7jItiwCEAgMB5cIgcTmFVPT47kIl7p8XY5VT5jqJpuYZavRH+Hlr8+tClMldEUnCOWYuo25QCVxMncjRfJ2ZDIQi4dWKU3KU4NL3RsljkkexyeQshSTHgEAAOMiZyRDtOFeGS/v5ONT+LHG5qHAw9txNLFZD9Y8AhAJwHh8jRmMwiknMqMCbKV+5SHF6ot+X29Uo7nZGXuoYBhwBwHhwiR5NWVI26BhOG9LHfu1gcRdOCm8XVepkrISkx4BCAxtvEGXCIHEZydgUAdHodIWrJRW15KyypNshcCUmJAYcANI7B4Tw4RA4jOacCUX5u0HVi7htqXdPcP0XswXEqDDgEgIOMiRzNrjNFGBdtn1PkOyqDkX/lORNOnEAAGHCIHMnZ4hqkF9Xg8dmD5C7FacwdGoLYEE+5yyAJMeAQAMtEf7yLisgx7E0rgVIhYHL/1he7pM57K2Gk3CWQxHiJigBYBhnzLioix5BbXodATy3cNPwblagtDDgEwHKJivmGyDHkV9YjSOcidxlEdo0Bhyx4mziRwyiorEcwAw7RRTHgEIDGHhy5iyCiDjlbXINIPze5yyCyaww4BAAQwB4cIkdQ32BCTnkd+ga4y10KkV1jwCEAHIND5CgySmogikC/AA+5SyGyaww4BMBymzgDDpH9SyusAQD0ZcAhuigGHALAxTaJHMWx3Ar4uKnh666RuxQiu8aAQwCaxuDIXQURXUx9gwlfHMzClcND5S6FyO4x4BAALtVA5AjWH8pBWa0Bt0+KlrsUIrvHgEMAGsfgyF0EEbXJbBbxv93pmDU4GFH+vIOKqD0MOASASzUQ2bsdpwuRXlSDJVPYe0PUEQw4BKBpkLHcVRBRW75OzMbgEB1GRfrKXQqRQ2DAIQBNq4kz4RDZoxq9EdtOFeKqERxcTNRRDDgEABDApRqI7NXWU4WobzBj7tAQuUshchgMOATAMgbHzPvEiezSj0dyMTzcG+G+XH+KqKMYcAgAl2ogsldms4gdp4swJy5Y7lKIHAoDDgHgbeJE9koEYDCa4ceZi4k6hQGHAFjuouIgYyIichYMOATAslQD8w0RETkLBhwC0DjImAmHiIicBAMOAWic6E/uIoiIiCTCgEMAuFQDERE5FwYcsuBt4kRE5EQYcAgAx+AQEZFzYcAhAJalGjiRMREROQsGHAJg6cEBOA6HiIicAwMOAbDMZAxwLhwiInIODDgEwHKbOMDlGoiIyDkw4BAAy0zGAAcaExGRc2DAIQCW1cQBXqIiIiLnoJK7ALIPisaoyx6c7hNFEWYRMJlFmEURJrMIkyjCbD7/uanxc7MZ1q/NF2z3cdegj7er3E0hInJYDDgEwHKbONCxHhyx8Y24wSSiwWxGg9Fs+dxkbnyIMJrNMJlFGBvfsI2mxo9/3m4WYTJbnmP92mT+037Lm7/ZGg7Q7Otm25uCgnU7rMHCLF4YOJqCSNO+8881i7jg2ObHiaIlkFzYHmNj+xpM59smhbcTRkKtPN/J2nSHm2j9+oJ/PwFQKwUoFQqoFQKUCgEqpaJxmwC1UmH5qFBApRSgatxv2SdY9ymabqejdll+Bs5/zzeYzDAYm3/dbJ+p+c+KoWm/0fI9ZNl//nlN+w1Gs9xNJXJIDDgE4PxdVNet2mP9xW00i2gwmmFoDCzWX85ms80uZTW92aoUljdf60MQoBAAxYVfKxq3CeePa/r8YtvVSgVcFE3Pt2xTCgIEQYBSYXnehZ+ffwBKhWWfSiFYw4FKqbAEhgtCg/LC17Z+fb4mxQXHnK/Tsv+PjDK88PNJ/PWTQ7b5n3wRCgFQNYagpmCkavzoqlHCXaOEm0YFd+2fPmqUcNOq4KJSNGuf4k//L5QX/D8XBEusVigaA7blP+t2QbBsFxq3o/FrEecDZVOYvjBINNvXFCIav5fPB4nzz7EGjcaAYTCZG7/f/xRETE0/D5avpcixQuP3o6YxjKqVCsvXqvNfq5QKjO/ri+Hh3t1/QaJehAGHAADj+/ohYVwERADqxjc0tUrR/PM//RK+8HOVUmj8JW35XN0YUFTWwNI8uKgUApTK5tub3vR6u/gIH1w/OgwNJss76IX/S5o+bfr/dOHgcJNZRINZhKkxhFqDqkls7GUynz/mgl6zpmMs+8zW3rk/H1NrMKHWYESN3vKxqEqPcyVG1BhMqNVbPtY1mKw9anJc7bwwMKiavj8VAtSq80FN08r3slalgIdWZf0eVquElue5MIioFC32ay78uVApmv08tLVPyR4zIpthwCEAQJDOBS9cM1TuMqiRt5tG7hK6TWw2/uiCsUaNlwJFNI75svwHy6di40fL80Wx5XYBaBa+LwwgDAxE1IQBh4hsQhAsPXj8JUNEcuBt4kREROR0GHCIiIjI6TDgEBERkdNhwCEiIiKnw4BDRERETqdbAeell16CIAhYtmyZdduaNWswdepU6HQ6CIKA8vLybp8TAKZOnWqZAOyCxz333NOd8omIiMhJdTngHDx4EKtXr8awYcOaba+trcXs2bOxYsUKyc7ZZMmSJcjLy7M+XnnllS7VTkRERM6tSwGnuroaCQkJWLt2LXx8fJrtW7ZsGR5//HGMHz9esnM2cXNzQ3BwsPWh0+m6Uj4RERE5uS4FnHvvvRdz587FjBkzJCukI+f85JNP4O/vj7i4OCxfvhy1tbVtHqvX61FZWdnsQURERL1DpycZ/fzzz3Ho0CEcPHhQsiI6cs6bbroJkZGRCA0NxdGjR/HYY48hJSUF69evb/X4lStX4plnnpGsRiIiInIcnQo4WVlZePDBB7F582a4uLhIUkBHz3nXXXdZPx86dChCQkIwffp0pKWloV+/fi2OX758OR5++GHr15WVlQgPD5ekZiIiIrJvnQo4iYmJKCwsxMiRI63bTCYTdu3ahTfffBN6vR5KpbJTBXT1nOPGjQMApKamthpwtFottFptp2ohIiIi59CpgDN9+nQkJyc323bbbbdh0KBBeOyxxzodbrpzzqSkJABASEhIp1+TiIiInFunAo6npyfi4uKabXN3d4efn591e35+PvLz85GamgoASE5OhqenJyIiIuDr6wvAEmquueYa3HfffR06Z1paGj799FNcccUV8PPzw9GjR/HQQw9hypQpbd5S/meiKAIABxsTERE5kKb37ab38Y7q9CDj9qxatarZ4N4pU6YAANatW4fFixcDsASW4uLiDp9To9Fgy5YtePXVV1FTU4Pw8HAsWLAATzzxRIfPUVVVBQAch0NEROSAqqqq4OXl1eHjBbGzkchBmc1m5ObmwtPTE4IgyFJD00DnrKysXjmHT29uf29uO8D29+b29+a2A727/VK1XRRFVFVVITQ0FApFx2e3kbwHx14pFAqEhYXJXQYAQKfT9bpv9Av15vb35rYDbH9vbn9vbjvQu9svRds703PThIttEhERkdNhwCEiIiKnw4DTg7RaLZ566qleOz9Pb25/b247wPb35vb35rYDvbv9cre91wwyJiIiot6DPThERETkdBhwiIiIyOkw4BAREZHTYcAhIiIip8OAI6HTp09j/vz58Pf3h06nw+TJk7F9+3br/pKSEsyePRuhoaHQarUIDw/Hfffd1+H1sfR6PUaMGAFBEKyLjdoTW7X/qquuQkREBFxcXBASEoJbbrkFubm5tm5Op9mi/RkZGbjjjjsQHR0NV1dX9OvXD0899RQMBkNPNKnDbPVv/8ILL2DixIlwc3ODt7e3jVvRdbZqf2lpKRISEqDT6eDt7Y077rgD1dXVtm5Op7TX9iNHjmDhwoUIDw+Hq6srYmNj8dprr7V73kOHDmHmzJnw9vaGn58f7rrrLrtrO2C79rd3Xnthi/bv2LEDgiC0+jh48GCHa2PAkdC8efNgNBqxbds2JCYmYvjw4Zg3bx7y8/MBWGZTnj9/PjZs2IDTp0/j/fffx5YtW3DPPfd06PyPPvooQkNDbdmEbrFV+6dNm4Yvv/wSKSkp+Oabb5CWlobrrruuJ5rUKbZo/6lTp2A2m7F69WocP34c//3vf7Fq1SqsWLGip5rVIbb6tzcYDLj++uuxdOnSnmhGl9mq/QkJCTh+/Dg2b96MH3/8Ebt27cJdd93VE03qsPbanpiYiMDAQHz88cc4fvw4/v73v2P58uV488032zxnbm4uZsyYgZiYGOzfvx+bNm3C8ePHresZ2hNbtL8j57UXtmj/xIkTkZeX1+xx5513Ijo6GqNHj+54cSJJoqioSAQg7tq1y7qtsrJSBCBu3ry5zee99tprYlhYWLvn//nnn8VBgwaJx48fFwGIhw8flqJsydi6/Rf6/vvvRUEQRIPB0OV6pdaT7X/llVfE6OjoLtcqtZ5o+7p160QvL6/ulmoTtmr/iRMnRADiwYMHrds2btwoCoIg5uTkSFN8N3W17X/961/FadOmtbl/9erVYmBgoGgymazbjh49KgIQz5w5I03xErBV+7t63p5mq/b/mcFgEAMCAsRnn322U/WxB0cifn5+GDhwID788EPU1NTAaDRi9erVCAwMxKhRo1p9Tm5uLtavX49LL730oucuKCjAkiVL8NFHH8HNzc0W5XebLdt/odLSUnzyySeYOHEi1Gq1VOV3W0+1HwAqKirg6+srRdmS6Mm22yNbtX/v3r3w9vZu9hfrjBkzoFAosH//fsnb0RVdaTvQ/vewXq+HRqNptrCiq6srAGD37t3SNaCbbNX+rp63p9mq/X+2YcMGlJSU4LbbbutcgZ2KQ3RRWVlZ4qhRo0RBEESlUimGhISIhw4danHcjTfeKLq6uooAxCuvvFKsq6tr85xms1mcPXu2+Nxzz4miKIpnz561yx4cUbRN+5s8+uijopubmwhAHD9+vFhcXGyLJnSLLdvf5MyZM6JOpxPXrFkjZendZuu223MPjijapv0vvPCCOGDAgBbbAwICxLffflvS+rujo21v8vvvv4sqlUr85Zdf2jzm2LFjokqlEl955RVRr9eLpaWl4oIFC0QA4osvvmiLZnSZLdrflfPKxVbtv9CcOXPEOXPmdLo2Bpx2PPbYYyKAiz5Onjwpms1m8aqrrhLnzJkj7t69W0xMTBSXLl0q9unTR8zNzW12zry8PPHkyZPi999/Lw4ePFhcunRpm6//2muviZMmTRKNRqMoij0fcORuf5OioiIxJSVF/PXXX8VJkyaJV1xxhWg2m23VbCt7ab8oimJ2drbYr18/8Y477rBFU1uwp7bLEXDkbr+cAccWbRdFUUxOThb9/f2tf7BdzCeffCIGBQWJSqVS1Gg04iOPPCIGBQWJL730ki2a3Izc7e/seaUmd/svlJWVJSoUCvHrr7/udDsYcNpRWFgonjx58qIPvV4vbtmyRVQoFGJFRUWz58fExIgrV65s8/y//fabCKDNb9r58+eLCoVCVCqV1gcAUalUiosWLZK0ra2Ru/2tycrKEgGIe/bs6XK7Ospe2p+TkyP2799fvOWWW5qNS7Ale2m7KMoTcORu/7vvvit6e3s329bQ0CAqlUpx/fr13W/gRdii7cePHxcDAwPFFStWdKqW/Px8saqqSqyurhYVCoX45Zdfdrt97ZG7/V39npKK3O2/0LPPPisGBAR0acylqnMXtHqfgIAABAQEtHtcbW0tADS7Ztz0tdlsbvN5Tfv0en2r+19//XU8//zz1q9zc3Mxa9YsfPHFFxg3bly7dXWX3O2X6jldZQ/tz8nJwbRp0zBq1CisW7euxWvYij20XU5yt3/ChAkoLy9HYmKidTzDtm3bYDabbf6zL3Xbjx8/jssuuwy33norXnjhhU7VEhQUBAB477334OLigpkzZ3bq+V0hd/u7+j0lFbnb30QURaxbtw6LFi3q2pjLTkcialVRUZHo5+cnXnvttWJSUpKYkpIiPvLII6JarRaTkpJEURTFn376SXzvvffE5ORk8ezZs+KPP/4oxsbGipMmTbKeZ//+/eLAgQPF7OzsVl/HXsfg2Kr9+/btE9944w3x8OHDYkZGhrh161Zx4sSJYr9+/cT6+npZ2toaW7U/OztbjImJEadPny5mZ2eLeXl51oe9sOX3/rlz58TDhw+LzzzzjOjh4SEePnxYPHz4sFhVVdXj7WyLLds/e/ZsMT4+Xty/f7+4e/dusX///uLChQt7vI1t6Ujbk5OTxYCAAPHmm29u9v1bWFhoPU9rbX/jjTfExMREMSUlRXzzzTdFV1dX8bXXXuvxNl6MrdrfkfPaA1v++4uipScLjZfDuoIBR0IHDx4UL7/8ctHX11f09PQUx48fL/7888/W/du2bRMnTJggenl5iS4uLmL//v3Fxx57TCwrK7Mes337dhGAePbs2VZfw14Djijapv1Hjx4Vp02bJvr6+oparVaMiooS77nnnjYDoJxs0f5169a1eQ3cntjqe//WW29tte3bt2/vucZ1gK3aX1JSIi5cuFD08PAQdTqdeNttt9lVuBPF9tv+1FNPtfpvGBkZaT2mtbbfcsstoq+vr6jRaMRhw4aJH374YQ+2quNs1f72zmsvbNV+URTFhQsXihMnTuxybYIoimLn+32IiIiI7BfnwSEiIiKnw4BDRERETocBh4iIiJwOAw4RERE5HQYcIiIicjoMOEREROR0GHCIiIjI6TDgEBERkdNhwCEiIiKnw4BDRERETocBh4iIiJwOAw4RERE5nf8HCShctHrrQzkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RUN THIS ONLY IF WE FOUND POPULATED ISLANDS\n",
    "nSubGeoms = len(MAP.geoms)\n",
    "subGeoms = [MAP.geoms[n] for n in range(nSubGeoms)]\n",
    "subAreas = [subGeoms[n].area for n in range(nSubGeoms)]\n",
    "mainSubGeomNo = subAreas.index(np.max(subAreas))\n",
    "mainGeom = subGeoms[mainSubGeomNo]\n",
    "nonMainGeom = dummyPoly\n",
    "for geo in subGeoms:  #the \"nonMainGeom is all pieces of the state not in the biggest piece\n",
    "    if geo != mainGeom:\n",
    "        if nonMainGeom == dummyPoly:\n",
    "            nonMainGeom = geo\n",
    "        else:\n",
    "            nonMainGeom = nonMainGeom.union(geo)\n",
    "        \n",
    "print(\"Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\")\n",
    "hasIsland, isIsland = [False for c in range(nCounties)], [False for c in range(nCounties)]\n",
    "isIsland =  [False for t in range(nTracts)]\n",
    "islandXshift, islandYshift = [0. for t in range(nTracts)], [0. for t in range(nTracts)]\n",
    "islandList = list()\n",
    "for c in range(nCounties):\n",
    "    if countyGeom[c].intersects(nonMainGeom):  #this county contains an island.  Find all its island tracts\n",
    "        hasIsland[c] = True\n",
    "        if countyGeom[c].disjoint(mainGeom):  #need to connect the whole county\n",
    "            print(\"county\",c,\"is completely disconnected from mainland.  Move it to adjoin mainland\")\n",
    "            isIsland[c] = True\n",
    "            countyDist = [999999. for cc in range(nCounties)]  #default, to avoid picking island counties as closest to this one\n",
    "            for cc in range(nCounties):\n",
    "                if countyGeom[cc].intersects(mainGeom):\n",
    "                    countyDist[cc] = countyGeom[c].distance(countyGeom[cc].intersection(mainGeom) )\n",
    "            closestCounty = countyDist.index(np.min(countyDist))\n",
    "            p1, p2 = nearest_points(countyGeom[closestCounty],countyGeom[c])\n",
    "            for t in countyTractList[c]:\n",
    "                islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "            for t in countyTractList[c]:\n",
    "                plotPoly(tractGeom[t])\n",
    "                plotCenter(t,tractGeom[t])\n",
    "                plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                tractCP[t] = tractGeom[t].centroid\n",
    "                tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                plotPoly(tractGeom[t],0.2)\n",
    "        else: #move only the county's island tracts to connect with main geom\n",
    "            mainCountyGeom = countyGeom[c].intersection(mainGeom)\n",
    "            plotPoly(mainCountyGeom)\n",
    "            plotCenter(c,mainCountyGeom)\n",
    "            for t in countyTractList[c]:\n",
    "                if tractGeom[t].intersects(nonMainGeom) and t not in skipList:\n",
    "                    if tractGeom[t].intersects(mainCountyGeom):\n",
    "                        print(\"unit\",t,\"has island pieces.  We will reduce the tract to its main state component\")\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "                        tractGeom[t] = tractGeom[t].intersection(mainCountyGeom)\n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                    else:    \n",
    "                        isIsland[t] = True\n",
    "                        print(\"unit\",t,\"is a true island.  We will move it to adjoin the mainland in same county.\")\n",
    "                        islandList.append(t)\n",
    "                        if tractGeom[t].geom_type != dummyPoly.geom_type :  #island tract is multiple geoms.  collapse to its largest isle\n",
    "                            isleGeos = [geo for geo in tractGeom[t].geoms]\n",
    "                            isleAreas = [geo.area for geo in isleGeos]\n",
    "                            biggestIsleNo = isleAreas.index(np.max(isleAreas))\n",
    "                            tractGeom[t] = isleGeos[biggestIsleNo]\n",
    "                            tractCP[t] = tractGeom[t].centroid\n",
    "                        p1, p2 = nearest_points(mainCountyGeom, tractGeom[t])\n",
    "                        islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                        plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                        tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9a94118d-5863-4458-b77a-67d18cf01c2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\n",
      "This would need adjustment for multi-county islands like Michigan UP\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with island triage - RUN ONLY WITH ISLANDS\n",
    "print(\"If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\")\n",
    "print(\"This would need adjustment for multi-county islands like Michigan UP\")\n",
    "for c in range(nCounties):\n",
    "    if hasIsland[c] :  #rebuild the county geom with the translated islands\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in countyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "MAP = countyGeom[0]\n",
    "plotPoly(countyGeom[0])\n",
    "for c in range(1,nCounties):\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "    plotPoly(countyGeom[c])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4aebe4ee-ee7b-4bf3-8413-f315556d453e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing all county centerpoints, then plot them\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"computing all county centerpoints, then plot them\")\n",
    "countyCP  = list()\n",
    "cutCountyList = list()\n",
    "for c in range(nCounties):\n",
    "    cTL = countyTractList[c]\n",
    "    countyCP.append(getHDcp( [tractCP[v] for v in cTL], [tractPop[v] for v in cTL], [i for i in range(len(cTL) ) ] ) )\n",
    "    if countyCP[c].disjoint(countyGeom[c]):  #possible with weird-shaped county\n",
    "        countyCP[c] = nearest_points(countyGeom[c],countyCP[c])[0]\n",
    "    plotPoly(countyCP[c].buffer(0.03))\n",
    "    plotPoly(countyGeom[c],0.5)\n",
    "plotPoly(MAP)\n",
    "plt.show()   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "207f3a55-e361-4777-ae46-41fe3d54a4b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code block reads in a csv with whole districts to be cut out of the map\n",
      "For MO my input file says to cut out 0 districts out of 8\n",
      "There were no cut districts in MO\n"
     ]
    }
   ],
   "source": [
    "print(\"This code block reads in a csv with whole districts to be cut out of the map\")\n",
    "unclippedMAP = MAP\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "nCutDistricts = nCuts\n",
    "nStops = trimDF[\"nStopLines\"][DFrow]\n",
    "nDistricts = trimDF[\"nDistricts\"][DFrow]\n",
    "stopLines = list()  #the 'stoplines' stop wedge growth that hops across concave map areas (not currently used)\n",
    "cutCountyList = list()\n",
    "allCutLists, allCutPops = list(), list()\n",
    "print(\"For\",STATE,\"my input file says to cut out\",nCuts,\"districts out of\",nDistricts)\n",
    "if nCuts > 0:\n",
    "    cutList = list()\n",
    "    cutXs = ast.literal_eval(trimDF[\"cutXs\"][DFrow])\n",
    "    cutYs = ast.literal_eval(trimDF[\"cutYs\"][DFrow])\n",
    "    cutPoints = [Point(cutXs[i],cutYs[i]) for i in range(len(cutXs)) ]\n",
    "    for cutNo in range(nCuts):\n",
    "        print(\"performing cut no\",cutNo,\"for state\",STATE)  #first two cutPoints must form the cutLine\n",
    "        cutNotDone = True\n",
    "        thisCutPoints = [ cutPoints[int(4*cutNo)],cutPoints[int(4*cutNo+1)],cutPoints[int(4*cutNo+2)],cutPoints[int(4*cutNo+3)]  ]\n",
    "        while cutNotDone:\n",
    "            cutPoly = Polygon([thisCutPoints[0],thisCutPoints[1],thisCutPoints[2],thisCutPoints[3] ])\n",
    "            cutLine = LineString([thisCutPoints[0], thisCutPoints[1] ] )\n",
    "            cutList = list()\n",
    "            cutPop = 0.\n",
    "            for c in range(nCounties):\n",
    "                if cutPoly.intersects(countyGeom[c]):\n",
    "                    if cutPoly.contains(countyGeom[c]):\n",
    "                        cutList = cutList + countyTractList[c]\n",
    "                        cutPop += countyPop[c]\n",
    "                    else:\n",
    "                        for t in countyTractList[c]:\n",
    "                            if cutPoly.contains(tractCP[t]):\n",
    "                                cutList.append(t)\n",
    "                                cutPop += tractPop[t]\n",
    "                            if STATE == \"NC\":\n",
    "                                if t == 1828:\n",
    "                                    print(\"special for NC - include vtd 1828 from county 55 in the cut list for contiguity\")\n",
    "                                    cutList.append(t)\n",
    "                                    cutPop += tractPop[t]\n",
    "            print(\"the total proposed cutPop is\",cutPop,\". Compare to\",int(statePop/nDistricts) )\n",
    "\n",
    "            doIcut = input(\"enter 1 to apply the cut\")\n",
    "            if str(doIcut) == str(1):\n",
    "                cutNotDone = False\n",
    "                allCutLists.append(cutList)\n",
    "                allCutPops.append(cutPop)\n",
    "            else:\n",
    "                print(\"Here is the state and the last proposed cutPoly.\")\n",
    "                plotPoly(cutPoly)\n",
    "                plotPoly(MAP)\n",
    "                plt.show()\n",
    "                print(cutPoly)\n",
    "                oldPts = list(cutPoly.exterior.coords)\n",
    "                newPtXs = ast.literal_eval(input(\"enter alternate [ x0, x1 ] for the first two points\") )\n",
    "                newPtYs = ast.literal_eval(input(\"enter alternate [ y0, y1 ] for the first two points\") )\n",
    "                thisCutPoints = [Point(newPtXs[0],newPtYs[0]), Point(newPtXs[1],newPtYs[1]),oldPts[2], oldPts[3] ]\n",
    "allCutVTDs = list()\n",
    "for L in allCutLists:\n",
    "    for v in L:\n",
    "        allCutVTDs.append(v)\n",
    "if nCutDistricts == 0:\n",
    "    print(\"There were no cut districts in\",STATE)\n",
    "else:\n",
    "    print(\"here are your cut districts\")\n",
    "    plotPoly(MAP)\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        cutGeo = tractGeom[L[0]]\n",
    "        for v in L:\n",
    "            cutGeo=cutGeo.union(tractGeom[v])\n",
    "        plotPoly(cutGeo,2)\n",
    "        plotCenter(i,cutGeo)\n",
    "        plotCenter(allCutPops[i],Point(cutGeo.centroid.x,cutGeo.centroid.y-0.5) )\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c915415-6c44-4c43-9826-b565cebed95a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Write the vtd cutlists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Write the vtd cutlists to a file\")\n",
    "cutDistrictNo = list()\n",
    "for v in allCutVTDs:\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        if v in L:\n",
    "            cutDistrictNo.append(i)\n",
    "            break\n",
    "nbrDF = pd.DataFrame( {\"vtdNo\":allCutVTDs,\"cutDistrictNo\":cutDistrictNo} )\n",
    "outname = STATE+\"wholeDistrictCuts.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "nbrDF.to_csv(outpath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17721728-e5c0-4787-b430-9d7d94970667",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 districts\n"
     ]
    }
   ],
   "source": [
    "countyPop = [0. for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    countyPop[countyNo[t]] += tractPop[t]\n",
    "print(nDistricts,\"districts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3073320-8d92-45ba-beb2-fb8c08d7170d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed all whole districts.  Finalize stats before squishing in outcroppings.\n",
      "Of the total statePop 6154913.0 we ignore 0.0 as it is cut out of the map\n",
      "This excluded pop comes from a total of 0 tracts\n",
      "Our final adjusted number of state districts, excluded fixed cut-out is 8 rather than original 8\n",
      "Each district will be drawn to house 769364.125 = 1/ 8 of non-cutout state pop 6154913.0 vs original= 6154913.0\n",
      "Here is a county-level modified map with each county's fraction of a district pop\n",
      "working on county 0\n",
      "working on county 20\n",
      "working on county 40\n",
      "working on county 60\n",
      "working on county 80\n",
      "working on county 100\n",
      "here is the MO map excluding cut and unpop coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Removed all whole districts.  Finalize stats before squishing in outcroppings.\")\n",
    "trueCountyPop = [countyPop[c] for c in range(nCounties)]\n",
    "trueCountyGeom = countyGeom.copy()\n",
    "countyPop = [0. for c in range(nCounties)]\n",
    "trueStatePop, true_nDistricts = np.sum(trueCountyPop), nDistricts\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "# minTractPop = 4.5  #already defined above\n",
    "populatedTractList = list()\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "statePop, excludedPop = 0., 0.\n",
    "\n",
    "for t in range(nTracts):\n",
    "    trueCountyTractList[countyNo[t]].append(t)\n",
    "    if t in allCutVTDs or t in skipList:  #  or vtdPop[t] < minTractPop:  #need to keep low-pop vtds as VEST may have assigned votes to them\n",
    "        excludedPop += tractPop[t]\n",
    "    else:\n",
    "        populatedTractList.append(t)\n",
    "        countyTractList[countyNo[t]].append(t)\n",
    "        countyPop[countyNo[t]] += tractPop[t]\n",
    "        statePop += tractPop[t]\n",
    "        #tractPop[t] = max(tractPop[t],0.000001)  #avoid possible later div/zero errors for nil-vote vtds\n",
    "print(\"Of the total statePop\",statePop+excludedPop,\"we ignore\",excludedPop,\"as it is cut out of the map\") #,\n",
    "#\"or in sparsely populated areas (<\",minTractPop,\") per geom\")\n",
    "print(\"This excluded pop comes from a total of\",nTracts -len(populatedTractList),\"tracts\")\n",
    "true_nDistricts = nDistricts\n",
    "nDistricts -= nCutDistricts\n",
    "print(\"Our final adjusted number of state districts, excluded fixed cut-out is\",nDistricts,\"rather than original\",true_nDistricts)\n",
    "aDP = statePop/float(nDistricts)\n",
    "print(\"Each district will be drawn to house\",r3(aDP),\"= 1/\",nDistricts,\"of non-cutout state pop\",statePop,\"vs original=\",trueStatePop)\n",
    "print(\"Here is a county-level modified map with each county's fraction of a district pop\")\n",
    "\n",
    "vtdGeom = tractGeom.copy()\n",
    "nVTDs = len(vtdGeom)\n",
    "\n",
    "cutCountyList, uncutCountyList = list(), list()\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"working on county\",c)\n",
    "    if len(countyTractList[c]) == 0:  #no vtds added to county b/c all were in cut lists:\n",
    "        cutCountyList.append(c)\n",
    "    else:\n",
    "        uncutCountyList.append(c)\n",
    "        countyGeom[c] = tractGeom[countyTractList[c][0]]\n",
    "        for t in countyTractList[c]:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "uncutMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "print(\"here is the\",STATE,\"map excluding cut and unpop coastal tracts\")\n",
    "\n",
    "for c in uncutCountyList:\n",
    "    plotPoly(countyGeom[c])\n",
    "    FONTSIZE = 8 #11  #reduce for TX\n",
    "    if countyPop[c] / statePop < 0.02:\n",
    "        FONTSIZE = 5 #7 #reduce for TX\n",
    "        plotCenter(r3(countyPop[c]/aDP),countyGeom[c], FONTSIZE)\n",
    "    else:\n",
    "        plotCenter(round(countyPop[c]/aDP,2),countyGeom[c], FONTSIZE)\n",
    "plotPoly(trueMAP,0.1)\n",
    "for c in cutCountyList:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "    plotCenter(\"cut\",countyGeom[c],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0062ac21-3328-4616-99ed-70c53e62b308",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the map topology before any squish operations.  Plot countyPop/aDP\n",
      "Working on county  0 distances\n",
      "Working on county  20 distances\n",
      "Working on county  40 distances\n",
      "Working on county  60 distances\n",
      "Working on county  80 distances\n",
      "Working on county  100 distances\n",
      "the max LAT-scaled distance between counties is 6.14182\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter user-picked max district diameter, or 0 to accept 6.14182  0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on county  0 neighbors\n",
      "Working on county  20 neighbors\n",
      "Working on county  40 neighbors\n",
      "Working on county  60 neighbors\n",
      "Working on county  80 neighbors\n",
      "Working on county  100 neighbors\n",
      "I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#For corners and outcroppings, we fuse county clusters\n",
    "collapsedCountyList = list()  #VA has independent cities that we will reassign to parent counties\n",
    "print(\"Now finalize the map topology before any squish operations.  Plot countyPop/aDP\")\n",
    "preSquishCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "maxD = 0.\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"distances\")\n",
    "    if c not in cutCountyList:\n",
    "        plotPoly(countyGeom[c],0.2)\n",
    "        plotCenter(r3(countyPop[c]/aDP), countyGeom[c], 7 )\n",
    "        for cc in range(c+1, nCounties):\n",
    "            dist = getLongDist(countyCP[c],countyCP[cc],xScale)\n",
    "            if dist > maxD and cc not in cutCountyList:\n",
    "                maxD = dist\n",
    "                #print(c,cc,maxD)\n",
    "print(\"the max LAT-scaled distance between counties is\",r5(maxD))\n",
    "plotPoly(MAP)\n",
    "plotPoly(MAP.centroid.buffer(0.5*maxD))\n",
    "plt.show()\n",
    "inputD = float( input(\"enter user-picked max district diameter, or 0 to accept \"+str(r5(maxD))+\" \") )\n",
    "if inputD > 0:\n",
    "    maxD = inputD\n",
    "neighborCountyList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"neighbors\")\n",
    "    for cc in uncutCountyList:\n",
    "        if cc > c and cc not in cutCountyList:  \n",
    "            if countyGeom[c].intersects(countyGeom[cc]) :\n",
    "                neighborCountyList[c].append(cc)\n",
    "                neighborCountyList[cc].append(c)\n",
    "print(\"I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\")\n",
    "hasFewNeighborCs, has1neighborCs = list(), list()\n",
    "plotPoly(MAP,0.15)\n",
    "for c in uncutCountyList:\n",
    "    if len(neighborCountyList[c]) == 2:\n",
    "        hasFewNeighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.2,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.5)\n",
    "    if len(neighborCountyList[c]) < 2:\n",
    "        has1neighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.1,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "398fbc6d-3b05-4c1f-87cb-72d11cdd98bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f9b3df93-d821-44ca-b4f4-b65d13baaffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continuing from above, develop corner county blocks from each low-pop corner county until it hits 0.25 of 769364\n",
      "checking if county 114 with pop 301578.0 and only 1 neighbor is less pop than 192341 vs districtPop 769364\n",
      "county 114 has pop 301578.0 and its only neighbor has pop 1004125.0 vs districtPop 769364.125 . Default = keep un-fused\n",
      "enter 0 to keep 114 as an unfused collection of units, 1 to fuse as one unit and stop, 2 to force-add at least the next closest county\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "0, 1, or 2.  Any other input taken as a zero 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking if county 2 with pop 5305.0 and 1 or 2 neighbors is less pop than 192341\n",
      "checking if county 59 with pop 23303.0 and 1 or 2 neighbors is less pop than 192341\n",
      "checking if county 77 with pop 15661.0 and 1 or 2 neighbors is less pop than 192341\n",
      "checking if county 66 with pop 12577.0 and 1 or 2 neighbors is less pop than 192341\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below I plot these again by county no, with faded neighboring counties\n",
      "0   [2, 43, 73, 1, 112, 37, 10, 31, 40, 30, 24]\n",
      "1   [59, 72, 4, 54, 103]\n",
      "2   [77, 34, 71, 102, 66, 11, 99, 17]\n",
      "3   [66, 99, 71, 102, 15, 8]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are 769364.125 192341\n",
      "You may suggest [[2,43,73,1,112,37], [59,72,4,48,54,103], [77,34,71,66,99,102], [22,98,55,51] ] \n",
      "let's decide whether to delete, accept, or modify list [2, 43, 73, 1, 112, 37, 10, 31, 40, 30, 24] with pop 190632.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) [2, 43, 73, 1, 112, 37, 10, 31, 40]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, I will replace [2, 43, 73, 1, 112, 37, 10, 31, 40, 30, 24] with [2, 43, 73, 1, 112, 37, 10, 31, 40]\n",
      "let's decide whether to delete, accept, or modify list [59, 72, 4, 54, 103] with pop 185562.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's decide whether to delete, accept, or modify list [77, 34, 71, 102, 66, 11, 99, 17] with pop 187018.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) [77,34,71,66,99,102]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, I will replace [77, 34, 71, 102, 66, 11, 99, 17] with [77, 34, 71, 66, 99, 102]\n",
      "let's decide whether to delete, accept, or modify list [66, 99, 71, 102, 15, 8] with pop 188019.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) 0\n",
      "enter 1 if you want to manually add another corner-county cluster 1\n",
      "Type in a [county list], where the corner county is first in the list.  Or type 0 to stop [22,98,55,51]\n",
      "enter 1 if you want to manually add another corner-county cluster 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our final pops and fused-county lists are...\n",
      "161018.0 [2, 43, 73, 1, 112, 37, 10, 31, 40]\n",
      "185562.0 [59, 72, 4, 54, 103]\n",
      "139686.0 [77, 34, 71, 66, 99, 102]\n",
      "25126.0 [22, 98, 55, 51]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aDP = float(statePop)/nDistricts\n",
    "maxCCBratio = 1./4.  #adjustable max fraction of district pop for corner county blocks.  Let's try 3/16\n",
    "maxCCBpop = maxCCBratio * aDP\n",
    "print(\"continuing from above, develop corner county blocks from each low-pop corner county until it hits\",r3(maxCCBratio),\"of\",int(aDP) )\n",
    "CCBlist, CCBpop, passThruList = list(), list(), list()  #\"waiveThru\" counties get chance to join a cluster even if high pop\n",
    "nFuses = 0\n",
    "for c in set(has1neighborCs).difference(set(collapsedCountyList)):\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and only 1 neighbor is less pop than\",int(maxCCBpop),\"vs districtPop\",int(aDP) )\n",
    "    if countyPop[c] > maxCCBpop:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        nc = neighborCountyList[c][0]\n",
    "        plotPoly(countyGeom[nc],0.5)\n",
    "        plotPoly(MAP,0.2)\n",
    "        print(\"county\",c,\"has pop\",countyPop[c],\"and its only neighbor has pop\",countyPop[nc],\"vs districtPop\",aDP,\". Default = keep un-fused\")\n",
    "        print(\"enter 0 to keep\",c,\"as an unfused collection of units, 1 to fuse as one unit and stop, 2 to force-add at least the next closest county\")\n",
    "        fuseChoice = input(\"0, 1, or 2.  Any other input taken as a zero\")\n",
    "        if fuseChoice == 0:\n",
    "            keepUnfused = True\n",
    "            break\n",
    "        elif int(fuseChoice) == 1:\n",
    "            CCBlist.append([c])\n",
    "            CCBpop.append(countyPop[c])  \n",
    "            hasFewNeighborCs.append(c)  #this adds this [c] to the final list of fused units, but will fail to find more in below\n",
    "        elif int(fuseChoice) == 2:\n",
    "            CCBlist.append([c,nc ] )\n",
    "            CCBpop.append(countyPop[c] + countyPop[nc])\n",
    "            hasFewNeighborCs.append(c)\n",
    "            passThruList.append(c)   #pass on thru to the below 2neighbor logic even if too high-pop to qualify\n",
    "    else:\n",
    "        hasFewNeighborCs.append(c)  #normal case; we'll handle in next, more general, for loop            \n",
    "        \n",
    "for c in set(hasFewNeighborCs).difference(set(collapsedCountyList)):\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and 1 or 2 neighbors is less pop than\",int(maxCCBpop) )\n",
    "    if countyPop[c] < maxCCBpop :\n",
    "        CCBlist.append([c])\n",
    "        CCBpop.append(countyPop[c])\n",
    "        CCBno = CCBlist.index([c])\n",
    "    if c in passThruList:\n",
    "        CCBno = CCBlist.index([c,neighborCountyList[c][0] ])\n",
    "    if countyPop[c] < maxCCBpop or c in passThruList:            \n",
    "        CCBstarter = c\n",
    "        canAdd = True\n",
    "        while canAdd:\n",
    "            nbrSet = set()\n",
    "            for cc in CCBlist[CCBno]:\n",
    "                nbrSet = ( nbrSet.union(set(neighborCountyList[cc])) ).difference(set(CCBlist[CCBno]))\n",
    "            nPop = [countyPop[cc] for cc in nbrSet]\n",
    "            nDist = [countyGeom[cc].centroid.distance(countyGeom[CCBstarter].centroid) for cc in nbrSet]\n",
    "            idx = np.argsort(nDist)\n",
    "            nbrList, newList = list(nbrSet), list()\n",
    "            for i in range(len(nDist)):  #add counties, closest to starter that will fit until over\n",
    "                cc = nbrList[idx[i]]\n",
    "                if CCBpop[CCBno] + countyPop[cc] < maxCCBpop:\n",
    "                    newList.append(cc)\n",
    "                    CCBlist[CCBno].append(cc)\n",
    "                    CCBpop[CCBno] += countyPop[cc]\n",
    "                    #print(\"adding county\",cc,\"with pop\",countyPop[cc],\"to list started by\",CCBstarter,\".Cluster pop now\",CCBpop[CCBno]  )\n",
    "            if newList == list():  #no eligible neighbor counties found; stop\n",
    "                canAdd = False\n",
    "                for cc in CCBlist[CCBno]:\n",
    "                    plotPoly(countyGeom[cc])\n",
    "                    plotCenter(CCBno,countyGeom[cc])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "                    \n",
    "print(\"Below I plot these again by county no, with faded neighboring counties\")\n",
    "plotPoly(MAP,0.15)\n",
    "for L in CCBlist:\n",
    "    print(CCBlist.index(L),\" \",L)\n",
    "    for c in L:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        for cc in list(set(neighborCountyList[c]).difference(L) ):\n",
    "            plotPoly(countyGeom[cc],0.4)\n",
    "            plotCenter(cc,countyGeom[cc],8)\n",
    "plt.show()\n",
    "rejectList = list()\n",
    "print(\"Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are\",r3(aDP), int(maxCCBpop))\n",
    "print(\"You may suggest [[2,43,73,1,112,37,10,31,40], [59,72,4,54,103], [77,34,71,66,99,102], [22,98,55,51] ] \")\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if L[0] not in passThruList:  #if we forced the fused-counties list above, don't give option here to modify\n",
    "        print(\"let's decide whether to delete, accept, or modify list\",L,\"with pop\",CCBpop[i] )\n",
    "        isOK = input(\"enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list)\")\n",
    "        if not (isOK == 1 or isOK == str(1)) :\n",
    "            if isOK == 0 or isOK == str(0) :\n",
    "                rejectList.append(L)\n",
    "            else:\n",
    "                notGood = True\n",
    "                while notGood:       \n",
    "                    Lnew = ast.literal_eval(isOK)        \n",
    "                    if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                        notGood = False\n",
    "                    else:\n",
    "                        print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                        isOK = input(\"Try again here \")\n",
    "                print(\"OK, I will replace\",L,\"with\",Lnew)\n",
    "                CCBpop[i] = np.sum( [countyPop[c] for c in Lnew] )\n",
    "                CCBlist[i] = Lnew.copy()\n",
    "for L in rejectList:\n",
    "    del CCBpop[ CCBlist.index(L)]\n",
    "    del CCBlist[CCBlist.index(L)]\n",
    "stillAdding = True\n",
    "while stillAdding:\n",
    "    addMore = input(\"enter 1 if you want to manually add another corner-county cluster\")\n",
    "    if int(addMore) != 1:\n",
    "        stillAdding = False\n",
    "    else:\n",
    "        isOK = input(\"Type in a [county list], where the corner county is first in the list.  Or type 0 to stop\")\n",
    "        if isOK == 0 or isOK == str(0) :\n",
    "            print(\"OK, we'll stop adding corner clusters\")\n",
    "            stillAdding = False\n",
    "        else:\n",
    "            notGood = True\n",
    "            while notGood:       \n",
    "                Lnew = ast.literal_eval(isOK)        \n",
    "                if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                    notGood = False\n",
    "                    for L in CCBlist:\n",
    "                        if set(L).intersection(set(Lnew)) != set(list()) :\n",
    "                            notGood = True\n",
    "                            print(\"OOPS! The following inputted counties are already in\",L,\":\",set(L).intersection(set(Lnew)) )\n",
    "                            isOK = input(\"Try again here \")\n",
    "                    if notGood == False:\n",
    "                        CCBlist.append(Lnew)\n",
    "                        CCBpop.append( np.sum( [countyPop[c] for c in Lnew] ) )\n",
    "                else:\n",
    "                    print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                    isOK = input(\"Try again here \")\n",
    "print(\"Our final pops and fused-county lists are...\")\n",
    "allFusedCounties = list()\n",
    "CCBgeom = [dummyPoly for L in CCBlist ]\n",
    "CCBcp = list()\n",
    "for i, L in enumerate(CCBlist):\n",
    "    CCBcp.append( getHDcp([countyCP[c] for c in L], [countyPop[c] for c in L], [ j for j in range(len(L)) ] ) )\n",
    "    print(CCBpop[i],L)\n",
    "    pops, CPs = list(), list()\n",
    "    for c in L:\n",
    "        if c == L[0]:\n",
    "            CCBgeom[i] = countyGeom[c]\n",
    "        else:\n",
    "            CCBgeom[i] = CCBgeom[i].union(countyGeom[c])\n",
    "        allFusedCounties.append(c)\n",
    "        pops += countyPop[c]       #  IS THIS EVEN USED?\n",
    "        CPs.append(countyCP[c])   #  IS THIS EVEN USED?\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(i,countyGeom[c])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "206c9372-8ec8-4190-93ed-04df286466e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now identify the border counties + border fused units, and interior small counties with pop < 15387\n",
      "We defined a total of 37 unit (but not corner-cluster) counties in the map\n",
      "excluding the cut-out districts and collapsed counties\n"
     ]
    }
   ],
   "source": [
    "maxWholeBorderCountyPop = 0.02 * aDP   #to encourage contiguity, border counties > 0.02 aDP will be forced whole\n",
    "maxInteriorBorderCountyPop = 0.02 * aDP\n",
    "print(\"now identify the border counties + border fused units, and interior small counties with pop <\", int(maxInteriorBorderCountyPop))\n",
    "unitCounties, borderCounties = list(), list()\n",
    "origMAP = MAP\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    MAPexterior = MAP.exterior\n",
    "else:\n",
    "    maxArea = 0\n",
    "    for geo in MAP.geoms:\n",
    "        if geo.area > maxArea:\n",
    "            MAPexterior = geo.exterior\n",
    "            maxArea = geo.area\n",
    "            MAP0 = geo\n",
    "    MAP = MAP0\n",
    "for c in set(uncutCountyList).difference(set(collapsedCountyList)):\n",
    "    if countyGeom[c].intersects(MAPexterior):\n",
    "        borderCounties.append(c)\n",
    "        if c not in allFusedCounties:\n",
    "            if countyPop[c] < maxWholeBorderCountyPop:\n",
    "                unitCounties.append(c)\n",
    "    else:\n",
    "        if countyPop[c] < maxInteriorBorderCountyPop and c not in allFusedCounties:\n",
    "            unitCounties.append(c)\n",
    "print(\"We defined a total of\",len(unitCounties),\"unit (but not corner-cluster) counties in the map\")\n",
    "print(\"excluding the cut-out districts and collapsed counties\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e0ff719c-59dc-4827-a522-11bd352f7524",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this state, do we have a lot of populated fragmented VTDs?\n",
      "151 fragmented VTDs out of 4604\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FYI, here are the locations of fragmented VTDs with pop > 3000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"For this state, do we have a lot of populated fragmented VTDs?\")\n",
    "nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "print(len(fragmentedVTDs),\"fragmented VTDs out of\",nVTDs)\n",
    "fragPop = [tractPop[v] for v in fragmentedVTDs]\n",
    "plt.hist(fragPop)\n",
    "plt.show()\n",
    "print(\"FYI, here are the locations of fragmented VTDs with pop > 3000\")\n",
    "for v in fragmentedVTDs:\n",
    "    if tractPop[v] > 3000:\n",
    "        plotPoly(vtdGeom[v])\n",
    "plotPoly(MAP,0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "71d771f8-dbe2-48f9-a702-9f66a62d1174",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\n",
      "I split up 151 fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\n",
      "looking for surrounds, now working on vtd 0\n",
      "looking for surrounds, now working on vtd 500\n",
      "looking for surrounds, now working on vtd 1000\n",
      "looking for surrounds, now working on vtd 1500\n",
      "looking for surrounds, now working on vtd 2000\n",
      "looking for surrounds, now working on vtd 2500\n",
      "looking for surrounds, now working on vtd 3000\n",
      "looking for surrounds, now working on vtd 3500\n",
      "looking for surrounds, now working on vtd 4000\n",
      "looking for surrounds, now working on vtd 4500\n",
      "After surround checks, we appear to have 4023 building blocks defined. We captured 268 surrounded VTDs\n",
      "working on unit neighbor lists for county 0\n",
      "WARNING - unit 174  = vtd 206 in county 0 has only 1 neighbor= [173] . Optional triage in next block\n",
      "working on unit neighbor lists for county 20\n",
      "working on unit neighbor lists for county 40\n",
      "working on unit neighbor lists for county 60\n",
      "WARNING - unit 1051  = vtd 1545 in county 63 has only 1 neighbor= [1050] . Optional triage in next block\n",
      "WARNING - unit 1058  = vtd 1551 in county 63 has only 1 neighbor= [1057] . Optional triage in next block\n",
      "working on unit neighbor lists for county 80\n",
      "working on unit neighbor lists for county 100\n",
      "All done creating unit lists\n"
     ]
    }
   ],
   "source": [
    "#3/2/24 - split up all multipoly VTDs into fragments, divvy pop by area\n",
    "unitPop, unitCP, unitGeom, nbrUnitGeom, allUnits = list(), list(), list(), list(), list()\n",
    "vtdUnits, countyUnits, borderUnits = list(),list(),list()\n",
    "countyUnitList = [list() for c in range(nCounties) ]\n",
    "surroundedVTDs, surrounders = list(), list()  #vtds that are surrounded by another vtd - problem for later enclaves, xfer their pops to surrounders\n",
    "#These surrounded VTDs don't get transferred to the unit list\n",
    "print(\"Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\")\n",
    "for c in unitCounties:\n",
    "    unitCP.append(countyCP[c])\n",
    "    unitPop.append(countyPop[c])\n",
    "    unitGeom.append(   countyGeom[c] )\n",
    "    nbrUnitGeom.append(countyGeom[c] )\n",
    "    countyUnits.append(c)\n",
    "    allUnits.append(c+0.5)  #use non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nVTDneighbors = [0 for v in range(nVTDs)] #this loop -- just checking for surrounded VTDs\n",
    "lastVTDneighbor = [-999 for v in range(nVTDs)]\n",
    "\n",
    "nFragmentedVTDs,fragmentedVTDs, coastalFragmentedVTDs = 0, list(), list()  #a prev version treated coastal separately\n",
    "fragVTDgeom, parentVTDno, fragVTDpop = vtdGeom.to_list(), [v for v in range(nVTDs)], tractPop.to_list()  #these lists will expand to include vtd fragments \n",
    "origVTDgeom = vtdGeom.copy() #we create a parallel fragVTDgeom list which grabs the 1st fragment, then append fragments to the total list\n",
    "origVTDpop = tractPop.copy()\n",
    "VTDchildren = [[v] for v in range(nVTDs)]  #the list of all fragment numbers associated with the original VTD, including ones that get surrounded\n",
    "countyFragVTDset = [set(countyTractList[c]) for c in range(nCounties)]\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "            geos, areas = [geo for geo in vtdGeom[v].geoms ], [geo.area for geo in vtdGeom[v].geoms]\n",
    "            for i, geo in enumerate(geos):\n",
    "                if i == 0:\n",
    "                    fragVTDgeom[v] = geo\n",
    "                    fragVTDpop[v] = tractPop[v] * areas[i] / np.sum(areas)  #overwrite, then distribute balance below\n",
    "                else:\n",
    "                    fNo = len(fragVTDgeom)\n",
    "                    fragVTDgeom.append(geo)\n",
    "                    fragVTDpop.append( tractPop[v] * areas[i] / np.sum(areas) )\n",
    "                    parentVTDno.append(v)\n",
    "                    countyFragVTDset[c].add(fNo )\n",
    "                    VTDchildren[v].append(fNo)\n",
    "                    nVTDneighbors.append(0)\n",
    "                    lastVTDneighbor.append(-999)\n",
    "                    \n",
    "print(\"I split up\",nFragmentedVTDs,\"fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\")\n",
    "\n",
    "debugV = -7616\n",
    "for kk,V in enumerate(populatedTractList):\n",
    "    if kk%500 == 0:\n",
    "        print(\"looking for surrounds, now working on vtd\",V)\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties:\n",
    "        for v in VTDchildren[V]:\n",
    "            for vv in countyFragVTDset[c].difference({v}) :\n",
    "                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                    nVTDneighbors[v] +=1\n",
    "                    lastVTDneighbor[v] = vv\n",
    "                    if v == debugV:\n",
    "                        print(\"I just added neighbor\",vv,\"to magicV\",v)\n",
    "                #if nVTDneighbors[v] >= 4:  #Enough to have >=2 even after surrounds.  Will do full neighbor list later\n",
    "                #    break\n",
    "            #if STATE == \"TN\":  #Tennessee has a lot of discontiguous county fragments.  Force the nearest intracounty to be a neighbor\n",
    "            #    if nVTDneighbors[v] == 0:\n",
    "            #        listMinusSelf = list( countyFragVTDset[c].difference({v}) )\n",
    "            #        fragDist = [fragVTDgeom[vvv].distance(fragVTDgeom[v]) for vvv in listMinusSelf ]\n",
    "            #        minIndex = fragDist.index(np.min(fragDist))\n",
    "            #        vv = listMinusSelf[minIndex]\n",
    "            #        nVTDneighbors[v] +=1\n",
    "            #        lastVTDneighbor[v] = vv\n",
    "            #        print(\"I am forcing an intracounty neighbor for VTDfrag\",v,\"of vtd\",V,\"in county\",c,\"to be VTDfrag\",vv)\n",
    "            #        forcedPairList.append([v,vv])\n",
    "            if nVTDneighbors[v] < 4:  #OK if a vtd has only 1 intracounty neighbor IF it touches another county, it's not surrounded\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if fragVTDgeom[v].intersects(countyGeom[cc]):\n",
    "                        if cc not in unitCounties + allFusedCounties:\n",
    "                            for vv in countyFragVTDset[cc] :\n",
    "                                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                                    nVTDneighbors[v] +=1\n",
    "                                    lastVTDneighbor[v] = vv\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                print(\"WARNING. vtd frag\",v,\"in county\",c,\"intersected county\",cc,\"but had no intracounty neighbors\")\n",
    "                                plotPoly(fragVTDgeom[v],2)\n",
    "                                plotCenter(\"iso\",fragVTDgeom[v])\n",
    "                                plotPoly(countyGeom[c])\n",
    "                                plotPoly(countyGeom[cc],0.2)\n",
    "                                plotCenter(cc, countyGeom[cc])\n",
    "                                plt.show()\n",
    "                        else:\n",
    "                            nVTDneighbors[v] +=1\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                if STATE == \"TN\":  #for TN, we tolerate some discontiguous counties\n",
    "                                    print(\"vtd fragment\",v,\"with no neighbors in its county\",countyNo[parentVTDno[v]],\n",
    "                                          \"borders a unit or fused county\",cc)\n",
    "                                else:\n",
    "                                    raise Exception(\"ERROR! Neighborless vtd fragment\",v,\"neighbors a unit or fused county\",cc)                                \n",
    "            if v == debugV:\n",
    "                print(v,\"has\",nVTDneighbors[v],\"neighbors.  its last is\",lastVTDneighbor[v])\n",
    "                \n",
    "for loop in range(3): #to catch surrounds of surrounds\n",
    "    for V in populatedTractList:\n",
    "        c = countyNo[V]\n",
    "        if c not in allFusedCounties and c not in unitCounties: \n",
    "            for v in VTDchildren[V]:\n",
    "                if nVTDneighbors[v] == 1:\n",
    "                    vv = lastVTDneighbor[v]\n",
    "                    if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                        for sNo, s in enumerate(surroundedVTDs):\n",
    "                            if surrounders[sNo] == v:\n",
    "                                surrounders[sNo] = vv\n",
    "                    surroundedVTDs.append(v)\n",
    "                    surrounders.append(vv)\n",
    "                    nVTDneighbors[vv] -=1\n",
    "                    nVTDneighbors[v] -=1 #in case this surrounder becomes a later surroundee\n",
    "                    fragVTDpop[vv] += fragVTDpop[v]\n",
    "                    fragVTDpop[v] = 0\n",
    "                    if lastVTDneighbor[vv] == v:  #find another neighbor in case this surrounder is also a surroundee in loop 2\n",
    "                        for vvv in countyFragVTDset[c]:\n",
    "                            if vvv not in [v,vv]:\n",
    "                                if fragVTDgeom[vvv].intersects(fragVTDgeom[vv]):\n",
    "                                    lastVTDneighbor[vv] = vvv\n",
    "                    if lastVTDneighbor[vv] == v:\n",
    "                        print(\"WARNING.  We did not find another neighbor of surrounder\",vv,\"other than surroundee\",v)\n",
    "                    if loop > 0:\n",
    "                        print(\"On loop\",loop+1,\"for county\",c,\" we found that vtd frag\",v,\"now has only 1 neighbor\",vv)\n",
    "if STATE == \"WI\":  #state-specific manual surround enforcement\n",
    "    specialSurrounded = [1610, 1611]\n",
    "    specialSurrounder = 2901\n",
    "    print(\"special for\",STATE,\", I believe that\", specialSurrounded,\"are surrounded by vtd (fragment)\",specialSurrounder,\". Here, let me show you\")\n",
    "    c = countyNo[parentVTDno[specialSurrounder]]\n",
    "    plotPoly(countyGeom[c], 0.5)\n",
    "    for v in specialSurrounded:\n",
    "        plotPoly(fragVTDgeom[v])\n",
    "        plotCenter(v,fragVTDgeom[v])\n",
    "    plotPoly(fragVTDgeom[specialSurrounder],0.2)\n",
    "    plotCenter(specialSurrounder, fragVTDgeom[specialSurrounder], 8)\n",
    "    plt.show()\n",
    "    shouldIcombine = int(input(\"enter 1 to apply the surrounding operation\"))\n",
    "    if shouldIcombine == 1:\n",
    "        for v in specialSurrounded:\n",
    "            vv = specialSurrounder\n",
    "            if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                for sNo, s in enumerate(surroundedVTDs):\n",
    "                    if surrounders[sNo] == v:\n",
    "                        surrounders[sNo] = vv\n",
    "            surroundedVTDs.append(v)\n",
    "            surrounders.append(vv)\n",
    "            nVTDneighbors[vv] -=1\n",
    "            nVTDneighbors[v] = 0 #we are capturing all surroundees (some of whom may touch other surroundees)\n",
    "            fragVTDpop[vv] += fragVTDpop[v]\n",
    "            fragVTDpop[v] = 0\n",
    "\n",
    "\n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] >1:\n",
    "                unitCP.append(fragVTDgeom[v].centroid)\n",
    "                unitGeom.append(fragVTDgeom[v])\n",
    "                unitPop.append(fragVTDpop[v])   #for now, ratio pop by area, not block decomposition\n",
    "                vtdUnits.append(v)   #if a border unit, we'll catch in below big loop, after checking for surround\n",
    "                allUnits.append(v)\n",
    "#for V in populatedTractList:\n",
    "#    c = countyNo[V]\n",
    "#    if c not in allFusedCounties and c not in unitCounties:  \n",
    "#        for v in VTDchildren[V]: \n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] == 1:  #a surrounded VTD, transfer its pop to surrounder unit.  Don't bother with shifting centerpoints\n",
    "                print(\"somehow we missed 1-neighbor vtd frag\",v,\" with last vtd frag nbr\",lastVTDneighbor[v])\n",
    "            if nVTDneighbors[v] == 0 and v not in surroundedVTDs:\n",
    "                print(\"WARNING. unsurrounded vtd\",v,\"had no found neighbors\")\n",
    "\n",
    "for i, L in enumerate(CCBlist):\n",
    "    unitCP.append( CCBcp[i] )\n",
    "    unitPop.append(CCBpop[i])\n",
    "    unitGeom.append(   CCBgeom[i])\n",
    "    nbrUnitGeom.append(CCBgeom[i])\n",
    "    allUnits.append(i+0.25)  #use alt non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nUnits = len(allUnits)\n",
    "print(\"After surround checks, we appear to have\",nUnits,\"building blocks defined. We captured\",len(surroundedVTDs),\"surrounded VTDs\")\n",
    "\n",
    "unitNbrs = [list() for u in range(nUnits)]\n",
    "countyUnitList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    for v in countyFragVTDset[c]:\n",
    "        if v in allUnits:\n",
    "            countyUnitList[c].append(allUnits.index(v))\n",
    "\n",
    "sketchyList = list()\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"working on unit neighbor lists for county\",c)\n",
    "    if c not in allFusedCounties:  #see a separate loop below for cluster-cluster neighbors\n",
    "        if c in unitCounties:    \n",
    "            u = allUnits.index(c+0.5)\n",
    "            for cc in neighborCountyList[c]:\n",
    "                if cc not in allFusedCounties:\n",
    "                    if cc in unitCounties:\n",
    "                        unitNbrs[u].append(allUnits.index(cc+0.5))  #we'll catch the complement in the cc county's loop\n",
    "                    else:\n",
    "                        for uu in countyUnitList[cc] :\n",
    "                            if countyGeom[c].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu)\n",
    "                                unitNbrs[uu].append(u)\n",
    "            for i,geo in enumerate(CCBgeom):\n",
    "                if geo.intersects(countyGeom[c]):\n",
    "                    unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                    unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "        else:  #non-unit county      \n",
    "            for u in countyUnitList[c] :\n",
    "                for uu in list( set(countyUnitList[c]).difference({u}) ) :\n",
    "                    if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                        unitNbrs[u].append(uu )  #will catch complement later in this county's loop\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if cc not in allFusedCounties and cc not in unitCounties: #see an above block for handling unitCounties\n",
    "                        for uu in countyUnitList[cc]:\n",
    "                            if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu )\n",
    "\n",
    "            for u in countyUnitList[c] :\n",
    "                for i,geo in enumerate(CCBgeom):\n",
    "                    if geo.intersects(unitGeom[u]):\n",
    "                        unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                        unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "                if len(unitNbrs[u]) == 0:\n",
    "                    print(\"ERROR - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has no neighbors\")\n",
    "                if len(unitNbrs[u]) == 1:  #after fragmentation, this unit appears surrounded.  Next block to confirm it has non-intracounty neighbors                       \n",
    "                    print(\"WARNING - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has only 1 neighbor=\",unitNbrs[u],\". Optional triage in next block\")\n",
    "                    sketchyList.append([u,unitNbrs[u][0] ] )\n",
    "\n",
    "CCBunits = CCBlist.copy()                        \n",
    "for i, geo in enumerate(CCBgeom):  #this loop: only cluster-cluster intersections.  Cluster-vtd and cluster-county neighbors already found above.\n",
    "    for ii in range(i+1,len(CCBgeom) ) :\n",
    "        if geo.intersects(CCBgeom[ii]):\n",
    "            if STATE != \"WA\" or i * ii != 3 :  #In WA, we don't allow ferry connection of Island to Clallam+Jefferson\n",
    "                unitNbrs[allUnits.index(i+0.25) ].append(allUnits.index(ii+0.25) ) #all CCB-county, CCB-vtd intersxns already covered\n",
    "                unitNbrs[allUnits.index(ii+0.25)].append(allUnits.index(i+0.25) )\n",
    "print(\"All done creating unit lists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "94741893-6835-45ac-9793-64cbaf4823b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sketchy unit 174 has neighbor list [173, 16, 18, 27, 32, 35]\n",
      "sketchy unit 1051 has neighbor list [1050, 28]\n",
      "sketchy unit 1058 has neighbor list [1057, 22, 28]\n"
     ]
    }
   ],
   "source": [
    "if len(sketchyList) == 0:\n",
    "    print(\"Great! all units appear to have multiple neighbors; no checks needed.\")\n",
    "for L in sketchyList:  #hope that some of these flagged units picked up unit-county or CCB neighbors\n",
    "    print(\"sketchy unit\",L[0], \"has neighbor list\",unitNbrs[L[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cb4277b4-d6ef-4c8c-aa23-6fde227379ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if allUnits[u] - int(allUnits[u]) == 0.25:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8378940d-4387-4188-8ae1-a62010500ecb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now build the border topology\n",
      "counties and clusters done, now working on (frag) vtd-based units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Now build the border topology\")\n",
    "borderUnits, borderType = list(), list()\n",
    "for c in borderCounties:\n",
    "    if c in unitCounties:\n",
    "        borderUnits.append(allUnits.index(c+0.5))\n",
    "        borderType.append(\"county\")\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if CCBgeom[i].intersects(MAPexterior):  #should usually be the case, but exception in VA\n",
    "        borderUnits.append(allUnits.index(i+0.25))\n",
    "        borderType.append(\"cluster\")\n",
    "print(\"counties and clusters done, now working on (frag) vtd-based units\")\n",
    "for c in borderCounties:\n",
    "    if c not in unitCounties + allFusedCounties:\n",
    "        for u in countyUnitList[c]:\n",
    "            if unitGeom[u].intersects(MAPexterior):\n",
    "                borderUnits.append(u)\n",
    "                borderType.append(\"vtd\")                \n",
    "if STATE == \"WA\":\n",
    "    print(\"adding a border unit between Island and Jefferson Counties, as these are only ferry-connected\")\n",
    "    borderUnits.append(2647)\n",
    "    borderType.append(\"vtd\")\n",
    "\n",
    "for i,b in enumerate(borderUnits):\n",
    "    plotPoly(unitGeom[b])\n",
    "    if borderType[i] == \"cluster\":\n",
    "        j = int(allUnits[b])\n",
    "        for c in CCBlist[j]:\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "            plotCenter(\"fused\",countyGeom[c], 6)\n",
    "for c in unitCounties:\n",
    "    plotPoly(countyGeom[c],0.4)\n",
    "    plotCenter(\"unit\",countyGeom[c],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "937d923d-9e95-45e9-9aef-77cb4377781d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking that the list of borderUnits is contiguous all around\n",
      "number of border units = 152\n"
     ]
    }
   ],
   "source": [
    "print(\"checking that the list of borderUnits is contiguous all around\")\n",
    "print(\"number of border units =\",len(borderUnits))\n",
    "for u in borderUnits:\n",
    "    isUnbroken, smallPieceList = isContiguous(list(set(borderUnits).difference({u})),unitNbrs)\n",
    "    if not isUnbroken:\n",
    "        print(\"border is discontig if we skip\",u)\n",
    "        for uu in smallPieceList:\n",
    "            plotPoly(unitGeom[uu],0.5)\n",
    "            plotCenter(\"n\"+str(uu),unitGeom[uu])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        plotPoly(unitGeom[u])\n",
    "        plt.show()\n",
    "        print(\"above is with unit numbers; below is VTDfrag nos\")\n",
    "        for uu in smallPieceList:\n",
    "            vv = allUnits[uu]\n",
    "            plotPoly(unitGeom[uu],0.5)\n",
    "            plotCenter(\"v\"+str(vv),unitGeom[uu],8)\n",
    "        v = allUnits[u]\n",
    "        plotCenter(v,unitGeom[u],8)\n",
    "        plotPoly(unitGeom[u])\n",
    "        plt.show()\n",
    "borderSet = set(borderUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "93603f77-77fa-4453-b54b-5ea07fcb6721",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here, we identify 'border' units that aren't needed to define the map border\n",
      "Bueno! no 'border units' that could be eliminated from the border set\n"
     ]
    }
   ],
   "source": [
    "print(\"here, we identify 'border' units that aren't needed to define the map border\")\n",
    "canBeRemoved, powerNeighbors = set(), set()\n",
    "for u in borderUnits:\n",
    "    uNeighbors = list(borderSet.intersection(set(unitNbrs[u])) )\n",
    "    if len(uNeighbors) < 2:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        uu = uNeighbors[0]\n",
    "        otherBorderNeighbors = set(unitNbrs[uu]).intersection(borderSet).difference({u})\n",
    "        if len(otherBorderNeighbors) > 1:  #this neighbor of our 1-border-neighbor border unit makes a border chain without the problem border unit\n",
    "            canBeRemoved.add(u)\n",
    "            powerNeighbors.add(uu)\n",
    "        plotCenter(uu,unitGeom[uu],6)\n",
    "        plotPoly(unitGeom[uu],0.5)\n",
    "        for uuu in set(unitNbrs[uu]).intersection(borderSet).difference({u}):\n",
    "            plotPoly(unitGeom[uuu])\n",
    "            plotCenter(str(uuu)+\"=N of\"+str(uu),unitGeom[uuu])\n",
    "        for uuu in set(unitNbrs[u]).difference(borderSet):\n",
    "                plotCenter(uuu+0.1,unitGeom[uuu],6)\n",
    "                plotPoly(unitGeom[uuu],0.1)\n",
    "        c = countyNo[allUnits[u]]\n",
    "        #plotPoly(countyGeom[c] )\n",
    "        #plotCenter(c, countyGeom[c])\n",
    "        plt.show()\n",
    "if len(canBeRemoved) == 0:\n",
    "    print(\"Bueno! no 'border units' that could be eliminated from the border set\")\n",
    "else:\n",
    "    print(canBeRemoved,\"is the list of 1-neighbor 'border' units that can be eliminated from the border set\")\n",
    "    print(\"... as they are enveloped on the border; the enveloping border unit has two other map-border neighbors\")\n",
    "    yesRemove = input(\"enter 1 to remove these from the list of border units\")\n",
    "    if int(yesRemove) == 1:\n",
    "        canRemove = True\n",
    "        for uu in powerNeighbors:\n",
    "            testSet = (borderSet.difference(canBeRemoved)).difference({uu})\n",
    "            if not isContiguous(list(testSet),unitNbrs):\n",
    "                canRemove = False\n",
    "                print(\"We can't complete the border if unit\",uu,\"is not included in the ring\")\n",
    "        if canRemove:\n",
    "            borderSet = borderSet.difference(canBeRemoved)\n",
    "            borderList = list(borderSet)\n",
    "            borderUnits = list(borderSet)\n",
    "            print(\"Success! we removed\",canBeRemoved,\"from the list of borderUnits\")\n",
    "    else:\n",
    "        print(\"Fine, be that way.  Sheesh, I will keep them.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b13c68e3-33ed-48b3-acca-78012ce0bf61",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the final border set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "print(\"here is the final border set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "173bb175-312b-4d08-a5b5-80a1d5d70416",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(isContiguous(borderUnits,unitNbrs)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fc1ae6e1-ef52-412b-a966-e9739071af8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For safekeeping, write the unit topology to a file\n"
     ]
    }
   ],
   "source": [
    "date_ = \"25May24\"\n",
    "print(\"For safekeeping, write the unit topology to a file\")  \n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        #for i,uu in enumerate(surrounders):  #no longer tracked\n",
    "         #   if u == uu:\n",
    "         #       unitVTDlist[u].append(surroundedVTDs[i])\n",
    "                \n",
    "unitNo = [u for u in range(nUnits)]\n",
    "unitCPx, unitCPy = [unitCP[u].x for u in range(nUnits)], [unitCP[u].y for u in range(nUnits)]\n",
    "onBorder = [0 for u in range(nUnits)]\n",
    "for u in borderUnits:\n",
    "    onBorder[u] = 1\n",
    "nbrDF = pd.DataFrame( {\"unitNo\":unitNo,\"centroid x\":unitCPx,\"centroid y\":unitCPy,\"unitPop\":unitPop,\"onBorder\":onBorder,\n",
    "                       \"neighborList\":unitNbrs, \"unitVTDlist\":unitVTDlist, \"unitParentVTDno\": unitParentVTDno, \"allUnits\":allUnits} )\n",
    "outname = STATE+\"unitTopologies_\"+date_+\".csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "nbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d4baaea4-7af4-4407-aeb3-f4291d335f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "vtdGeom = tractGeom.copy()  #optional reset if we need to rerun the below clipPoly routine with alternate clipPolys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "abdd07e7-f9be-480a-9d3d-b3948eeaaa2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "toSquishCountiesList = list()   #default; if we did not have any clipPoly's to move in\n",
    "nClips = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4d5fdc3c-6126-4fa8-b484-1d0052c70aed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for many states we move in outcroppings via clipPoly's before running the wedgePoly solver\n",
      "adding code here to 'push in' state panhandles ...\n",
      "performing squish no 1 for state MO\n",
      "clipPoly 0 intersects [34, 66, 71, 77, 99, 102] counties and a total of 97 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": 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n+2Ncny6S5suzrmNulBxllRp8/Ux/jPeTNp9uYGEhIqJ2R6cT+O74ZXx2IBV9nG0Q9eoQuNh2kDT/P8cvY9WBVPh2s0HEy9Lm061YWIiIqF0pLldj/uY4HE+/hjmPumP+qJ4wkXD8/bUKNRZsjsextGK8PswdC0ZLm0+3x8JCRETtxon0a5i3KQ6AwMZZg/BIz3sPMG2Mkxl1+UIIbJg1CMMkzqc7Y2EhIqI2T6PV4YuDafj22CUEe9hh9XR/OFibS5Zfq9Xhy1/T8fXRDAx1t8PnT0mbT/fGwkJERG1azvUqzI2UIz5HgXfGeOG1R6Qdf59bVo2wSDnk2WV4e3QvzBnmzvH6rYCFhYiI2qx9iflYtDUB1uYm2PxaIAK6d5I0f39SAd6Jjv8jfwgCuttKmk8Nx8JCRERtjkqjxT/3pOCn01kY6+uEj6f4wcZCuvH3Ko0WK/ZewMZTVzGmtyM+neIPmw4cr9+aWFiIiKhNySgqR0iEHFeuVeKjSb54ZpCrpOPvM4oqEBIRi8vXKrH8SV88N1jafGoaFhYiImoThBDYci4HS3cmo1snC+wIGQovJ+muHSeEQPT5HLy/IxldO5pjx5tD4d2F16bTFywsRESk98pVGiz+JQk74vLw1AAXLH3CBx1MpfsVVqGuxeLtifglLg/TBzjjgyd6S5pPzcf/G0REpNcScsoQGilHSUUN1szoi4l9u0man5SrQEhELIrL1S2ST9JgYSEiIr0khMD3J67gk/0X4d1Fho2zBqF7Z0tJ8384mYmV+y7Ay0mGH18ahB520uWTtFhYiIhI75RUqLEwOgGHLxbhlYfdsHCMF0yNpRt/X1pZg4Vb4nHoYhFmB7th0ePS5pP0WFiIiEivnLpUgnmb5NBoBX6YORDDvRwkzT99uQTzouKgrtVi/cwBeMzLUdJ8ahksLEREpBdqtTp8dTgDaw+nY4hbZ3w5oy8cZdKNv9fqBNYeTsdXh9IxsIct1szoBycbjtdvK1hYiIio1eUrqhEWGYdzV0sxf2RPvDHcA0YSjr/PV1RjXlQcYjJLETaiJ0IekzafWh4LCxERtaqDKYVYGB0PCxMjRL0aiEFu0o6/P3ShEG9viYeZsREiXxmCwQ91ljSf7g8WFiIiahXqWi1W7r2IH3/PxEhvR6ya6odOlqaS5n+yLxXrT17BSG8HrJrqL2k+3V8sLEREdN9duVaJkIhYpBdW4IMJPngxqIek4++vXKtEaGQs0goqsHSCD2ZKnE/3HwsLERHdV9vlOVi8PQkOMnNseyMIvt1sJM3fEZeL97Ylwt7arEXyqXWwsBAR0X1Rqa7F+zuSsTU2B5P7d8OHE31hZSbdr6Gqmlos3ZGMLedzMKlfNyx/Utp8al38P0lERC0uOU+B0Ag5CpQqrJ7mjykBzpLmp+QpERIZi/wyFT6b5o+pEudT62NhISKiFiOEwMZTV/HRngvwcLDC7tBgPGRvJWn+T6evYvmeC3C3t8LuucFwlzCf9AcLCxERtYiyqhosjE7AwZRCzAzqgfBxXjAzNpIsX1GlwTtb43EguRAvBnZH+DhvmJtIl0/6hYWFiIgkF5NZirBIOao0Wnz3fABG93aSNP9cZinCouJQoa7FuucDMEbifNI/LCxERCQZrU7gmyMZ+OLXNAR074Q1M/qha0cLSfP/fewSPj+Yhn4uHbHm6X7oJmE+6S8WFiIikkShUoV5UXE4faUEocM9MHeEJ4yNpLsCcpFShbc2x+H3SyUIGe6BMInzSb+xsBARUbMdSS3C25vjYWRogJ9fHowgdztJ84+mFmHB5ngYGhrg59mDEeQhbT7pPxYWIiJqsppaHT77Xyq+++0yHu1lj9XT/NHZykyyfI22Ln/dscsY1tMeq6f7w07CfGo7WFiIiKhJskqqEBoZi5R8JRaP98asoW4wlPAKyNmlVQiNlCMpV4H3xnnh5eCHJM2ntoWFhYiIGm1XfB7e25aITpamiH49CP4uHSXN35OQj3e3JqCjpQmi5wShr8T51PawsBARUYNV12ixbFcyomKyMcG/K1ZM8oW1uYmk+R/uTkHk2Sz8w68LVkzuA5mE+dR2sbAQEVGDXCxQIjRCjuzrVfh0ih+mDXCW9ArIaYXlCImIRVZpFT6e3AdPDXThFZapHgsLERHdlRACEWez8OGuFLjZWWJXSDA8Ha0lzY+KycayXclwte2AnSHB6ClhPrUPLCxERATUqgFlHiB0gKYKcOoDAFBUaxC+LQF7Ewvw3BBXLB7vI+n4e6VKg/BtidiTkI9nBrtiyXgfWJhyvD7dioWFiOhBoakGyvMBIYCKQqCmCvjzHRcjU8C6C2BoBHR0BQDEZl1HaIQcSpUG3zzbH+P6dJF0deKyyxAaGYuySg2+fqY/xvtJm0/tCwsLEVF7pVEBOWcBbU3d18bmgJUTYGgIdPEHTC1v+zSdTuC7Y5fw2YFU9HG2QdSrQ+Bi20Gy1dLpBP5z/DJWHUiFbzcbRLwsbT61TywsRERtiUYFKHOBhpyMWlFcd9TEzrPB8cXlaszfHIfj6dcw51F3zB/VEyYSjr+/VqHGgs3xOJZWjNeHuWPBaGnzqf1iYSEiakvKsoDqUsDS/t7LWtoBVg4Njj6Rfg3zNsUBENg4axAe6dmA12iEkxl1+UIIbJg1CMMkzqf2jYWFiKgtMTCoO2rSqbtkkRqtDl8cTMO3xy4h2MMOq6f7w8HaXLL8Wq0OX/6ajq+PZmCoux0+f0rafHowsLAQEbUlBoZ1n+SRSM71KsyNlCM+R4F3xnjhtUekHX+fW1aNsEg55NlleHt0L8wZ5s7x+tQkLCxERG2OkCRlX2I+Fm1NgLW5CTa/FoiA7p0kyf3T/qQCvBMd/0f+EAR0t5U0nx4sLCxERG2NaF5hUWm0+OeeFPx0OgtjfZ3w8RQ/2FhIN/5epdFixd4L2HjqKsb0dsSnU/xh04Hj9al5WFiIiPRR1hlAVQYY/DFE7c93UQxNAEffJsdmFJUjJEKOK9cq8dEkXzwzyFXS8fcZRRUIiYjF5WuVWP6kL54bLG0+PbhYWIiI9FGtCnjoUcDYTJI4IQS2nMvB0p3J6NbJAjtChsLLSSZJ9p/50edz8P6OZHTtaI4dbw6Fdxfp8olYWIiI9JGhMaDTShJVrtJg8S9J2BGXh6cGuGDpEz7oYCrdj/8KdS2W/JKE7fJcTB/gjA+e6C1pPhHAwkJEpJ8MjQHR/MKSkFOG0Eg5SipqsGZGX0zs202ClbshKVeBkIhYFJerWySf6E8sLERE+sjQqFlHWIQQ+P7EFXyy/yK8u8iwcdYgdO98+1H8Tc3/4WQmVu67AC8nGX58aRB62EmXT/R3LCxERPrI0Bi4nglUWd/6mJkMsLrzlNiSCjUWRifg8MUivPKwGxaO8YKpsXTj70sra7BwSzwOXSzC7GA3LHpc2nyi22FhISLSR3aeQHnBrfdra4BraXcsLKculWDeJjk0WoEfZg7EcK+Gj+ZviNOXSzAvKg7qWi3WzxyAx7wcJc0nuhMWFiIifWRqCXR2v/X+WjVQVXLr3VodvjqcgbWH0zHErTO+nNEXjjLpxt9rdQJrD6fjq0PpGNjDFmtm9IOTDcfr0/3DwkJE1Nb8bXBcvqIaYZFxOHe1FPNH9sQbwz1gJOH4+3xFNeZFxSEmsxRhI3oi5DFp84kagoWFiKhNMcBfR/MfTCnEwuh4WJgYIerVQAxyk3b8/aELhXh7SzzMjI0Q+coQDH6os6T5RA3FwkJE1Aapa7VYufcifvw9EyO9HbFqqh86WZpKmv/JvlSsP3kFI70dsGqqv6T5RI3FwkJE1MbkK1R4edfvSC+swAcTfPBiUA9Jx99nXqtEaKQcqQXlWDrBBzMlzidqChYWIqI25Jf4XOzYkYAq2QBseyMIvt1sJM3fEZeL97Ylwt7arEXyiZqKhYWIqA2oVNfi/R3J2BGbifm9bLH2mWBYmUn3I7yqphZLdyRjy/kcTOrXDcuf9JU0n6i5+N1IRKTnkvMUCI2Qo0CpwqdT/DG581VAwjKRkqdESGQs8stU+GyaP6YGOEuWTSQVFhYiIj0lhMDGU1fx0Z4L8HCwwu7QYDxkaw5cvSpZ/k+nr2L5ngtwt7fC7rnBcLe3kiSbSGqNmqX87bffws/PDzKZDDKZDIGBgdi3bx8AIDMzEwYGBre9bdmy5Y6ZQgi8//776NKlCywsLDBy5Eikp6c3b6uIiNq4sqoavPp/57F0ZzKeGeyK7W8G4aH6MiHu+tyGUFRp8PpP57FkRzKeHuiC7W8EsayQXmvUERZnZ2d8/PHH8PT0hBACGzZswMSJEyGXy+Hl5YX8/Pyblv/uu++watUqjB079o6Zn376Kb766its2LABbm5uWLJkCcaMGYOUlBSYm3OKIhE9eGIySxEWKUeVRovvng/A6N5ONx6U4NM65zJLERYVhwp1LdY9H4Axf80n0lMGQohmVXVbW1usWrUKs2fPvuWxfv36oX///vj+++9v+1whBLp27YoFCxbg7bffBgAoFAo4Ojrixx9/xIwZMxq8HkqlEjY2NlAoFJDJZE3bGCKiVqTVCXxzJANf/JqGgO6dsGZGP3TtaHHzQjotcOU3wH14k/L/fewSPj+Yhn4uHbHm6X7o9vd8ovusob+/m3wOi1arxZYtW1BZWYnAwMBbHj9//jzi4uLw9ddf3zHjypUrKCgowMiRI+vvs7GxweDBg3Hq1Km7Fha1Wg21Wl3/tVKpbOKWEBG1vkKlCvOi4nD6SglCh3tg7ghPGBvd7l37ph1hKSpX4a1Ncfj9UglChnsg7I75RPqp0YUlMTERgYGBUKlUsLKywvbt2+Hj43PLct9//z28vb0RFBR0x6yCgrorkTo63ny1T0dHx/rH7mTlypVYtmxZY1efiEjvHEktwtub42FkaICfXx6MIHe7ezyjcQfGj6UVY8HmOBgYGODn2YMR5HGvfCL90+h63atXL8TFxeHMmTOYM2cOXnzxRaSkpNy0THV1NSIiIm77NpFUwsPDoVAo6m/Z2dkt9lpERC2hplaHFXsv4KUfYtDH2Qb7wh6+d1lpxDksGq0OK/ddwIvrz6J31z/yWVaojWr0ERZTU1N4eHgAAAICAhATE4M1a9Zg3bp19ctER0ejqqoKL7zwwl2znJzqTvQqLCxEly5d6u8vLCxE37597/pcMzMzmJmZNXb1iYj0QlZJFUIjY5GSr8Ti8d6YNdQNhg29AnIDTj3MLq1CaKQcSbkKvDfOCy8HP9TwfCI91Ow3MHU63U3nkgB1bwc98cQTsLe3v+tz3dzc4OTkhEOHDtXfp1QqcebMmdueF0NE1B7sis/D+K+O43qVBtGvB+HlhxtRJgxuvlrz7exJyMe4NcdRUqlG9JwgvPqIO8sKtXmNOsISHh6OsWPHwtXVFeXl5YiIiMDRo0dx4MCB+mUyMjLw22+/Ye/evbfN8PLywsqVKzFp0iQYGBhg3rx5+Oc//wlPT8/6jzV37doVTz75ZLM2jIhI31TXaLFsVzKiYrIxwb8rVkzyhbW5iaT5H+5OQeTZLPzDrwtWTO4DmYT5RK2pUYWlqKgIL7zwAvLz82FjYwM/Pz8cOHAAo0aNql9m/fr1cHZ2xujRo2+bkZqaCoVCUf/1O++8g8rKSrz66qsoKytDcHAw9u/fzxksRNSuXCxQIjRCjuzrVfh0ih+mDXBu+hWQb3OAJa2wHCERscgqrcLHk/vgqYEuvMIytSvNnsOiLziHhYj0kRACEWez8OGuFLjZWWLt0/3g6WjdvND0XwHPkfX5UTHZWLYrGa62HfCvZ/qjZ3Pzie6jFp/DQkREd6eo1iB8WwL2JhbguSGuWDzeB+YmRs0P/uPAiVKlQfi2ROxJyMczg12xZLwPLEwlyCfSQywsREQtIDbrOkIj5FCqNPjm2f4Y16fLvZ/UUAKIyy5DaGQsyio1+PqZ/hjvJ2E+kR5iYSEikpBOJ/Dd8cv47EAq+jjbIOrVIXCx7SBp/v7EPMw99zt8u9kg4mVp84n0FQsLEZFEisvVmL85DsfTr2HOo+6YP6onTJoz/j5hM9DBtv5LhaoW3x27jMM5wMuPDMKC0c3MJ2pDWFiIiCRwIv0a5m2KAyCwcdYgPNLz7nOoGqSDLeBRd3LtyYxrmLcjDkJ4YvVLfTFMinyiNoSFhYioGTRaHb44mIZvj11CsIcdVk/3h4O1BGMZtBrA0AS1Wh2+/DUdXx/NwFB3O3z+lET5RG0MCwsRURPlXK/C3Eg54nMUeGeMF157RMLx97VqFKuAOd+dhjy7DG+P7oU5wzixlh5cLCxERE2wLzEfi7YmwNrcBJtfC0RA906S5h9MKcDGHfHIN/fF5teGIKC77b2fRNSOsbAQETWCSqPFP/ek4KfTWRjr64SPp/jBxkK68fcqjRYr9l7A1lMX8YK7Df717MOw6cDx+kQsLEREDZRRVI6QCDmuXKvER5N88cwgV0nH32cUVSA0Uo5LxRX48B/eeMq1AgYsK0QAWFiIiO5JCIEt53KwdGcyunWywI6QofByku4SIEIIRJ/Pwfs7ktG1ozl2vDkU3p0AFKdK9hpEbR0LCxHRXZSrNPh/25OwMz4PTw1wwdInfNDBVLofnRXqWiz5JQnb5bmYPsAZHzzRuy5fpbj3k4keICwsRER3kJBThtBIOUoqarBmRl9M7NtN0vykXAVCImJRXK6+Nb99XJeWSDIsLEREfyOEwPcnruCT/Rfh3UWGjbMGoXtnS0nzfziZiZX7LsDLSYYfXxqEHna3yZfw/Biito6FhYjoL0oq1FgYnYDDF4vwysNuWDjGC6bG0o2/L62swcIt8Th0sQizg92w6PE75QvUX5aZiFhYiIj+dOpSCeZtkkOjFfhh5kAM93KQNP/05RLMi4qDulaL9TMH4DEvx7s/gX2FqB4LCxE98Gq1Onx1OANrD6djurMC7z7SGZ2MEoB0ABBAF3/AqunlRasTWHs4HV8dSsfAHrZYM6MfnGz+Ml4/+yygVv5tpdSATNpzZojaMhYWInqg5SuqERYZh3NXSzF/ZE+80TUdRj1HAIZGdeeQlBfU3ZpYWAoUKoRFyRGTWYqwET0R8pgHjP4+Xl+trL/IIRHdHgsLET2wDqYUYmF0PCxMjBD1aiAGudkCuUpAVQZY2tUtZGCIuvNJGu/QhUK8vSUeZsZGiHxlCAY/1FmydSd60LCwENEDR12rxcq9F/Hj75kY6e2IVVP90MnStO5Bi05AReHNhaWRHzFW12rxyb5UrD95BSO9HbBqqv+NfCJqEhYWInqgXC6uG3+fXliBDyb44MWgHjeP1y+5DDz06I2vDQwBobs55HomoFEBdj0Bw5s/4ZN5rRKhkXKkFpRj6QQfzPx7PhE1CQsLET0wtsXmYPEvSXCUmWPbG0Hw7WZz60JCBxj95UejgQFQeQ24fhUwNK675ckBW3cgYRPQ0QXo0hfQ1WJ//FWs3JsCB0tj7HqhN3o5GADXr9z7CA1nxBHdEwsLEbV7lepavL8jGVtjczC5fzd8ONEXVmZ3+vH3t/ZgagV06gFoNYCmCtDVAg4+gH0vwLE3UF4AVeYp/Pd0EfZdLMM/vBzx5mM90cHMEKitqTt5907+PPLi2FuKzSRq11hYiKhdS85TIDRCjgKlCqun+WNKgPPdn2BoXPeRYmOzuq+NTAD7nndY1ggpldYI2W2C/LLOWD51GKbeK5+ImoSFhYjaJSEENp66io/2XICHgxV2hwbjIXuruz9Jp6s7ilKWBdh53jP/p9NXsXzPBbjbW2H33GC43yufiJqMhYWI2p2yqhosjE7AwZRCzAzqgfBxXjAzvstbM3+6crTu7Z7O7nddTFGlwTtb43EguRAvBnZH+DhvmJs0IJ+ImoyFhYjalZjMUoRFylGl0eK75wMwurdTw5/coTNQXXbXRc5fLcXcyDhUqGux7vkAjGlMPhE1GQsLEbULWp3AN0cy8MWvaQjo3glrZvRD144WgCIHyDoNmNsAJhZAj+Bbn3xhF2BsUffnOxxd0eoE/n3sEj4/mIZ+Lh2x5ul+6NbRogW3iIj+ioWFiNq8QqUK86LicPpKCUKHe2DuCE8YG/0xH8W6C2BqCXiOAq4cByqKbh2zb2Jx19H4ReUqvLUpDr9fKkHIcA+E/TWfiO4LFhYiatOOpBZhweZ4GBsa4OeXByPI3e7mBQyNgA52dSfSOg8E0g8Ajr43HjcwALS1d8w/llaMBZvjYGBggJ9nD0aQh90dlyWilsPCQkRtUk2tDqsOXMR/jl/Bo73ssXqaPzpbmd1+YecBQOo+wHUIYO91Y/7Jn24zB0Wj1eGz/6Vi3bHLGNbTHqun+8PuTvlE1OJYWIiozckqqUJoZCxS8pVYPN4bs4a6wfDvV0D+KwODunH7V08CHV0B24fump9dWoXQSDmSchV4b5wXXg5+6O75RNTiWFiIqE3ZFZ+H97YlopOlKaJfD4K/S8eGPdG0Q915LIXJQPpBoPvQuvv+Zk9CPt7dmoCOliaInhOEvnfJF0LwOkFE9wkLCxG1CdU1WizblYyomGxM8O+KFZN8YW1u0vggx951R1mu/FZ3pOWPKbbVNVp8uDsFkWez8A+/LlgxuQ9kt8kvqipClaYK5TXlsDS1xEM2dz9aQ0TSYGEhIr13sUCJ0Ag5sq9X4dMpfpg2wLl5RzbMrIFejwN5cUBuLNKMPRESEYus0ip8PLkPnhroctv85JJk9JD1gEMHB6SUpLCsEN1HLCxEpLeEEIg4m4UPd6XAzc4Su0KC4eloXfegtha4nnnjBFqdFtBp6i5SqKv9478aQFtTN3Lf1u2Wcfuiiz+O7fkZr58phKttB+wMCUbPP/NvwxCGKK0uRWl1KWq0NS201UR0OywsRKSXFNUahG9LwN7EAjw3xBWLx/vcPP5eVVZXWDr/cZTDxBwwtK67eKGRMWBoUnfhQkMTwNAQuLj3psKiVGkQvi0R+YnFeDrAHe88MQgWpncfr+/d2bv+zy4yFwm3lojuhYWFiPRObNZ1hEbIoVRp8M2z/TGuT5dbFxK6ugFw9/jETz0zayDzJNBjKOKyy/BuxHF0qCpA+KMOGBjYA7hHWSGi1sXCQkR6Q6cTWPfbZaz+Xyr6ONsg6tUhcLG99ZM89coLAGOzuqMqZta3TrD9qx7B0F0+hu27d2PvqXj0tbfH3BmPoqur561zWYhI77CwEJFeKC5XY/7mOBxPv4Y5j7pj/qieMLnb+HtLe8Cpz43zVgoSAY8Rd1z8WmUNlh1W4/QlBaYMm4IFo++RT0R6hYWFiFrdifRrmLcpDoDAxlmD8EhP+3s/ycAAkP3lraLrV+646MmMunwhDLF61mgMa0g+EekVFhYiajUarQ5fHEzDt8cuIdjDDqun+8PB2rxpYeLWu2q1Onz5azq+PpqBoe52+PypZuQTUatiYSGiVpFzvQpzI+WIz1HgnTFeeO0Racff55ZVIyxSDnl2Gd4e3QtzhrlzvD5RG8bCQkT33b7EfCzamgBrcxNsfi0QAd07AUIAteq6m1YDaNV/zFDR3j7k7yfKGt2YSrs/qQCLtibAyswYm18bgoDuti24NUR0P7CwEFHLqLwG5Jyrm4nyhxqtQMSZqzh8sQgvd7fF7GA3WGpigQwBwAAwMq371I+RaV0BMTIFDI3qygxw50/zCAE49YFKo8WKvRew8dRVjOntiE+n+MOmQxPG9xOR3mFhIaKWoakGOvUAHLwAABlF5QiJkOPKtW54f+IoPDPIVdILB2YUVSA08ndcKq7A8id98dxgafOJqHWxsBBRyzAyBbTXIYTAlnM5WLozGd06WWBHyFB4OckkexkhBKLP5+D9Hcno2tEcO94cCu8u0uUTkX5gYSGilmFkgkpVNcKj4rAzPg9PDXDB0id80MFUuh87FepaLPklCdvluZg+wBkfPNFb0nwi0h/8m01ELSKpoArfbI7Fb9UPYc2MvpjYt5u0+bkKhETEorhc3SL5RKRfWFiISFJCCHx/4gq+2J+AJ+yAPa8Eo3tnS0nzfziZiZX7LsDLSYYfXxqEHnbS5RORfmJhISLJlFSosTA6AYcvFuHVYHcs7GkNEwnLSmllDRZuicehi0WYHeyGRY97wdSY4/WJHgQsLEQkiVOXSjBvkxwarcAPMwdiuJcDkPGrZPlnLpcgLCoO6lot1s8cgMe8HCXLJiL9x8JCRM1Sq9Xhq8MZWHs4HUPcOuPLGX3hKJNu/L1WJ7D2cDq+OpSOgT1ssWZGPzjZcLw+0YOGhYWImixfUY2wyDicu1qK+SN74o3hHjD66/j721zfpzEKFCqERckRk1mKsBE9EfLY3/KJ6IHBwkJETXIwpRALo+NhYWKEqFcDMchN2vH3hy4U4u0t8TAzNkLkK0Mw+KHOkuYTUdvCwkJEjaKu1WLl3ov48fdMjPR2xKqpfuhkaXr7hZtwMERdq8Un+1Kx/uQVjPR2wKqp/nfOJ6IHBgsLETXY5eIKhEbKkV5YgQ8m+ODFoB53H3/fyLeEMq9VIjRSjtSCciyd4IOZ98onogcGCwsRNci22Bws/iUJjjJzbHsjCL7dbCTN3xGXi/e2JcLe2qxF8omobWNhIaK7qlTX4v0dydgam4PJ/bvhw4m+sDJr6I+Oex9iqaqpxdIdydhyPgeT+nXD8icbk09EDwr+VCCiO0rOUyA0Qo4CpQqrp/ljSoCzpPkpeUqERMYiv0yFz6b5Y6rE+UTUfrCwENEthBDYeOoqPtpzAR4OVtgdGoyH7K2akHT780+EEPjp9FUs33MB7vZW2D03GO5NyieiBwULCxHdpKyqBgujE3AwpRAzg3ogfJwXzIyNJMtXVGnwztZ4HEguxIuB3RE+zhvmJtLlE1H71KiLcHz77bfw8/ODTCaDTCZDYGAg9u3bd9Myp06dwmOPPQZLS0vIZDI88sgjqK6uvmOmVqvFkiVL4ObmBgsLC7i7u2P58uUQopkTp4io0WIySzFuzXHEZJbiu+cD8METvSUtK+evlmLcV8dx+nIp1j0fgGUTfVlWiKhBGnWExdnZGR9//DE8PT0hhMCGDRswceJEyOVy9O7dG6dOncLjjz+O8PBwrF27FsbGxoiPj4eh4Z170SeffIJvv/0WGzZsQO/evXHu3Dm89NJLsLGxwdy5c5u9gUR0b1qdwDdHMvDFr2kI6N4Ja2b0Q9eOFpLm//vYJXx+MA39XDpizdP90E3CfCJq/wxEMw9l2NraYtWqVZg9ezaGDBmCUaNGYfny5Q1+/j/+8Q84Ojri+++/r79vypQpsLCwwE8//dTgHKVSCRsbGygUCshkskZtA9GDrFCpwryoOJy+UoLQ4R6YO8ITxkYSXQE5/VcUOQXjrU1x+P1SCUKGeyBMynwiavMa+vu7yT81tFotoqKiUFlZicDAQBQVFeHMmTNwcHBAUFAQHB0dMWzYMJw4ceKuOUFBQTh06BDS0tIAAPHx8Thx4gTGjh171+ep1WoolcqbbkTUOEdSizB2zXFcKq7Azy8PxvzRvSQtEwm5ZRi35jjSCivw8+zBWCBxPhE9OBp90m1iYiICAwOhUqlgZWWF7du3w8fHB6dPnwYAfPDBB/jss8/Qt29fbNy4ESNGjEBSUhI8PT1vm/fuu+9CqVTCy8sLRkZG0Gq1+Oijj/Dss8/edT1WrlyJZcuWNXb1iQhATa0Oqw5cxH+OX8Gjveyxepo/OluZSZav0erw2f9ScfF4Gnp7jMTq6f6wkzCfiB48jX5LqKamBllZWVAoFIiOjsZ///tfHDt2DGVlZRg6dCjCw8OxYsWK+uX9/Pwwfvx4rFy58rZ5UVFRWLhwIVatWoXevXsjLi4O8+bNw+eff44XX3zxjuuhVquhVqvrv1YqlXBxceFbQkT3kFVShdDIWKTkK7HocS/MGuoGQwmvgJxdWoXQSDmSchX4auA1PD7xeUnziah9aehbQo0+wmJqagoPDw8AQEBAAGJiYrBmzRq8++67AAAfH5+blvf29kZWVtYd8xYuXIh3330XM2bMAAD06dMHV69excqVK+9aWMzMzGBmxn+xETXGrvg8vLctEZ0sTRH9ehD8XTpKmr8nIR/vbk1AR0sTRM8JQl/VOYBlhYgk0Ow5LDqdDmq1Gj169EDXrl2Rmpp60+NpaWl3PR+lqqrqlk8RGRkZQafTNXfViOgP1TVaLNuVjKiYbEzw74oVk3xhbW4iaf6Hu1MQeTYL//DrghWT+0BmbgKkS/YSRPSAa1RhCQ8Px9ixY+Hq6ory8nJERETg6NGjOHDgAAwMDLBw4UIsXboU/v7+6Nu3LzZs2ICLFy8iOjq6PmPEiBGYNGkSQkJCAAATJkzARx99BFdXV/Tu3RtyuRyff/45Zs2aJe2WEj2gLhYoERohR/b1Knw6xQ/TBjhLegXktMJyhETEIqu0Ch9P7oOnBrrcyDcyBrS1df8lImqGRv0UKSoqwgsvvID8/HzY2NjAz88PBw4cwKhRowAA8+bNg0qlwltvvYXS0lL4+/vj4MGDcHd3r8+4dOkSrl27Vv/12rVrsWTJErzxxhsoKipC165d8dprr+H999+XaBOJHkxCCESczcKHu1LgZmeJXSHB8HS0ljQ/KiYby3Ylw9W2A3aGBKPn3/ONzYHaasBIutclogdTs+ew6AvOYSG6QVGtQfi2BOxNLMBzQ1yxeLyPpBNllSoNwrclYk9CPp4Z7Iol431gYXqb/Lw4QNYNsLKX7LWJqH1psZNuiUi/xWZdR2iEHEqVBt882x/j+nSRND8uuwyhkbEoq9Tg62f6Y7zfXfJNLIBalaSvT0QPJhYWonZCpxNY99tlrP5fKvo42yDq1SFwse0gaf5/jl/GqgOp8O1mg4iXG5BvaAxoayRbByJ6cLGwELUDxeVqzN8ch+Pp1zDnUXfMH9UTJhJOlL1WocaCzfE4llaM14e5Y8FoafOJiO6FhYWojTuRfg3zNsUBENg4axAe6Snt+SInM+ryhRDYMGsQhkmcT0TUECwsRG2URqvDFwfT8O2xSwj2sMPq6f5wsDaXLL9Wq8OXv6bj66MZGOpuh8+fakK+gQHQPs7rJ6JWxsJC1AblXK/C3Eg54nMUeGeMF1575CFJx9/nllUjLFIOeXYZ3h7dC3OGuTc9X8KZL0T04GJhIWpj9iXmY9HWBFibm2Dza4EI6N5J0vz9SQVYtDUBVmbG2PzaEAR0t216mIEhoNVIt3JE9MBiYSFqDUL8cdMCQnfrTaet+3SNVlP/KRt1rQ5fH83Ajrg8POlpj4WP94C1WSlQUtq41zYyrfu4sYkFYGwB/HFpDJVGixV7L2DjqasY09sRn07xh02HZo7vN7YA1OXNyyAiAgsLUfNc+Q2ovcPHdg0AiL/8GX98/eefDQz/uBn95c9/3AwN64qFkSlgaIzM0ios25mC7OtVmD+qF57w79qwd1pud/5ITSVQWVw3H0VTBQiBPIUKXx+9hHyFCv8d6ooRXmYwyDvWgPzbbOufDxgY1pUtS56kS0TNx8JC1BzaGsBzZIvFCyGw5VwOlu5MR7dOTvhXSD94OUk3yVkIgejzOXh/RzK6duyLf73RH95dJMrX6QBdbV1xISJqJhYWouYQqDuK0QInlparNPh/25OwMz4PTw1wwdInfNDBVLq/shXqWiz5JQnb5bmYPsAZHzzRW9J8GBoChqbS5RHRA42Fhag5TCzq3lYxtZQ0NiGnDKGRcpRU1GDNjL6Y2LebpPlJuQqERMSiuFzdIvlERFJjYSFqDiPTP06KlaawCCHw/Ykr+GT/RXh3kWHjrEHo3lm6MiSEwA8nM7Fy3wV4Ocnw40uD0MNO2rJFRNQSWFiImqNWVfdJGAmUVKixMDoBhy8W4ZWH3bBwjBdMjaU7/6O0sgbvRMfj1wtFmB3shkWPS5tPRNSSWFiImkOrBozNmh1z6lIJ5m2SQ6MV+GHmQAz3cpBg5W44c7kEYVFxUNdqsX7mADzm5ShpPhFRS2NhIWoOgWadcFur1eGrwxlYezgdQ9w648sZfeEok268vlYnsPZwOr46lI6BPWyxZkY/ONlIl09EdL+wsBC1knxFNcIi43Duainmj+yJN4Z7wEjC8foFChXCouSIySxF2IieCHlM2nwiovuJhYWoFRxMKcTC6HhYmBgh6tVADHJrxvj72zh0oRBvb4mHmbERIl8ZgsEPdZY0n4jofmNhIbqP1LVarNx7ET/+nomR3o5YNdUPnSylm1WirtXik32pWH/yCkZ6O2DVVH9J84mIWgsLC9F9crm4AqGRcqQXVuCDCT54MagHDCQcOJd5rRKhkXKkFpRj6QQfzJQ4n4ioNbGwEN0H22JzsPiXJDjKzLHtjSD4drORNH9HXC7e25YIe2uzFsknImptLCxELahSXYslO5KwLTYXk/t3w4cTfWFlJt1fu6qaWizdkYwt53MwqV83LH9S2nwiIn3Bn2xELSQ5T4HQCDkKlCqsnuaPKQHOkuan5CkRGhmLvDIVPpvmj6kS5xMR6RMWFiKJCSGw8dRVfLTnAjwcrLA7NBgP2VtJmv/T6atYvucC3O2tsHtuMNwlzCci0kcsLEQSKquqwcLoBBxMKcTMoB4IH+cFM2MjyfIVVRq8szUeB5IL8WJgd4SP84a5iXT5RET6ioWFqFlE/Z9iMksRFilHlUaL754PwOjeTpK+0vmrpZgbGYcKdS3WPR+AMRLnExHpMxYWomYxgFYn8M2RDHzxaxoCunfCmhn90LWjNBdEBOrG6//72CV8fjAN/Vw6Ys3T/dBNwnwioraAhYWoGa5X1eCN/57B6SslCB3ugbkjPGFsJN0VkIvKVXhrUxx+v1SCkOEeCJM4n4iorWBhIWqiI6lF2LojCZeM+uPnlwcjyN1O0vxjacVYsDkOBgYG+Hn2YAR5SJtPRNSWsLAQNVJNrQ6rDlzEf45fQairJZa98DA6W5lJlq/R6vDZ/1Kx7thlDOtpj9XT/WEnYT4RUVvEwkLUCFklVQiNjEVKvhKLx3tjlqMpDCUsE9mlVQiNlCMpV4H3xnnh5eCHYMgrLBMRsbAQNdSu+DyEb0uEraUpol8Pgr9LRyD9smT5exLy8e7WBHS0NEH0nCD0dekoWTYRUVvHwkJ0D9U1WizblYyomGxM8O+KFZN8YW1uAghx7yc3MP/D3SmIPJuFf/h1wYrJfSAzN5Ekm4iovWBhIbqLiwVKhEbIkX29Cp9O8cO0Ac43roCs1QBGzfsrlFZYjpCIWGSVVuHjyX3w1EAXXmGZiOg2WFiIbkMIgYizWfhwVwrc7CyxKyQYno7WNy+k0wCGTTsSIoRAVEw2lu1KhqttB+wMCUbPv+cTEVE9Fhaiv1FUaxC+LQF7Ewvw3BBXLB7vc/vx91oNYNT4wqJUaRC+LRF7EvLxzGBXvP+PO+QTEVE9Fhaiv4jNuo7QCDmUKg2+ebY/xvXpcueFdbWAYeP+CsVllyE0MhZllRp8/Ux/jPe7Sz4REdVjYSECoNMJrPvtMlb/LxV9nG0Q9eoQuNh2uPuTGnGERacT+O+Jy/h0fyp8u9kg4uUG5BMRUT0WFnrgFZerMX9zHI6nX8OcR90xf1RPmDRk/H0Dz2G5VqHGgs3xOJZWjNeHuWPB6AbmExFRPRYWeqAdTy/GW5viAQhsnDUIj/S0b/iTG3CE5WTGNczbFAchBDbMGoRhjcknIqJ6LCz0QNJodfj8YBr+fewSgj3ssHq6PxyszSXLr9Xq8OWv6fj6aAaGutvh86ekzScietCwsNADJ+d6FeZGyhGfo8A7Y7zw2iPSjr/PLatGWKQc8uwyvD26F+YMc+d4fSKiZmJhoQfKvsR8LNqaAGtzE2x+LRAB3TtJmr8/qQCLtibAyswYm18bgoDutpLmExE9qFhY6IGg0mjxzz0p+Ol0Fsb6OuHjKX6wsZBu/L1Ko8WKvRew8dRVPN7bCZ9M8YNNB47XJyKSCgsLtXsZReUIiZDjyrVKfDTJF88McpVm/L2BASAEMooqEBopx6XiCix/0hfPDZYon4iI6rGwULslhMCWczlYujMZ3TpZYEfIUHg5ySTMB/YlFWDBr+no2tEcO94cCu8u0uUTEdENLCzULpWrNPh/25OwMz4PTw1wwdInfNDBVLpv9wp1Lb7cewEHUwowIWAAPniit6T5RER0M/6EpXYnIacMoZFylFTUYM2MvpjYt5uk+Um5CoRExMK8vBhLxntjZLC/pPlERHQrFhZqN3Q6gfUnr+CT/Rfh3UWGjbMGoXtnS8nyhRD44WQmVu67AC8nGb6dPBDOHTlbhYjofmBhoXahpEKNt7fE40hqMV552A0Lx3jB1Fi68fellTV4Jzoev14owuxgNyx63AumiiuAAUfsExHdDyws1OadulSCeZvk0GgFfpg5EMO9HCTNP3O5BGFRcVDXarF+5gA85uVY94CuFjDhBQyJiO4HFhZqs2q1Onx1OANrD6djiFtnfDmjLxxl0r1Fo9UJrD2cjq8OpWNgD1usmdEPTjZ/yddqAHPOWiEiuh9YWKhNyldUIywyDueulmL+yJ54Y7gHjCQcf1+gUCEsSo6YzFKEjeiJkMduk6+rBQz5V4iI6H7gT1tqcw6mFGJhdDwsTIwQ9WogBrlJO/7+0IVCvL0lHmbGRoh8ZQgGP9T59guysBAR3Tf8aUtthrpWi5V7L+LH3zMx0tsRq6b6oZOlqaT5n+xLxfqTVzDS2wGrpvrfPZ+FhYjovuFPW2oTMrOzsCPqP8hWdMB/Al0w0scMBvm/AeIvC931HaE/HxR/+frGnwuUKnxz5BJyrldhXaALRvuYwSDvt7rFDI3/uJnU/dfojz9XlQJGPIeFiOh+YGEhvbctNgdf//IbnK16YuGcJ+DbzUbS/B1xuXjvl0TYW/fBv+b0vzlfCECnBXSauiMq2j/+q6sFTHsDRtId4SEiojtjYSG9VamuxZIdSdgWm4tXfTsibERPWHaRrqxU1dRi6Y5kbDmfg0n9umH5k76wMvvbXwkDg7ojKkb8q0JE1Jr4U5j0UnKeAqERchQoVVg9zR9TeqglzU/JUyI0MhZ5ZSp8Ns0fUwOcJc0nIiJpsbCQXhFCYOOpq/hozwV4OFhhd2gwHrK3Akou1R3tkCD/p9NXsXzPBbjbW2H33GC421tJsOZERNSSWFhIb5RV1WBhdAIOphRiZlAPhI/zgpmxUd2DQgCGRs3KV1Rp8M7WeBxILsSLgd0RPs4b5ibNyyQiovuDhYX0QkxmKcIi5ajSaPHd8wEY3dvp5gWEFjBs+hTb81dLMTcyDhXqWqx7PgBj/p5PRER6rVFXbvv222/h5+cHmUwGmUyGwMBA7Nu376ZlTp06hcceewyWlpaQyWR45JFHUF1dfdfc3NxcPPfcc+jcuTMsLCzQp08fnDt3rvFbQ22OView9lA6nlp3Ct06WWDv3IdvLStA3adyDBp/NESrE/j6SAamrzuNLjbm2Bv2MMsKEVEb1KgjLM7Ozvj444/h6ekJIQQ2bNiAiRMnQi6Xo3fv3jh16hQef/xxhIeHY+3atTA2NkZ8fDwMDe/ci65fv46hQ4di+PDh2LdvH+zt7ZGeno5OnTo1e+NIvxUqVZgXFYfTV0oQOtwDc0d4wtjoDt8rTRjSVlSuwlub4vD7pRKEDPdA2N3yiYhIrxkIIcS9F7szW1tbrFq1CrNnz8aQIUMwatQoLF++vMHPf/fdd3Hy5EkcP368OasBpVIJGxsbKBQKyGSyZmVRyzuSWoQFm+NhbGiAL2f0RZC73d2fkHse6OQGdGjYGP5jacVYsDkOBgYGWPNUXwR53COfiIhaRUN/fzf5n5tarRZRUVGorKxEYGAgioqKcObMGTg4OCAoKAiOjo4YNmwYTpw4cdecnTt3YsCAAZg2bRocHBzQr18//Oc//7nn66vVaiiVyptupP9qanX4aE8KXvohBn7ONtgX9vC9ywoA6HQNOulWo9Vh5b4LeHH9WfTu+kc+ywoRUZvX6MKSmJgIKysrmJmZ4fXXX8f27dvh4+ODy5cvAwA++OADvPLKK9i/fz/69++PESNGID09/Y55ly9fxrfffgtPT08cOHAAc+bMwdy5c7Fhw4a7rsfKlSthY2NTf3NxcWnsptB9llVShWn//h0//p6JxeO9sf7FgehsZdawJzfgLaHs0ipM+/cpfH/8Ct4b54UfZg6EXUPziYhIrzX6LaGamhpkZWVBoVAgOjoa//3vf3Hs2DGUlZVh6NChCA8Px4oVK+qX9/Pzw/jx47Fy5crb5pmammLAgAH4/fff6++bO3cuYmJicOrUqTuuh1qthlp9Y5iYUqmEi4sL3xLSU7vi8xC+LRG2lqZY+3Q/+Lt0bFxA5gmg2wDA5PafFNqTkI93tyago6UJ1j7dH30bm09ERK2ioW8JNfpjzaampvDw8AAABAQEICYmBmvWrMG7774LAPDx8blpeW9vb2RlZd0xr0uXLrd9ztatW++6HmZmZjAz47+e9V11jRbLdiUjKiYbE/y7YsUkX1ibN+GCgTrtbY+wVNdo8eHuFESezcI//LpgxeQ+kDUln4iI9Fqz57DodDqo1Wr06NEDXbt2RWpq6k2Pp6WlYezYsXd8/tChQ2/7nO7duzd31aiVXSxQIjRCjuzrVfh0ih+mDXCGQVOn1epqbzmHJa2wHCERscgqrcLHk/vgqYEuTc8nIiK91qjCEh4ejrFjx8LV1RXl5eWIiIjA0aNHceDAARgYGGDhwoVYunQp/P390bdvX2zYsAEXL15EdHR0fcaIESMwadIkhISEAADeeustBAUFYcWKFZg+fTrOnj2L7777Dt999520W0r3jRACEWez8OGuFLjZWWJXSDA8Ha2bm1o/ml8IgaiYbCzblQxX2w7YGRKMns3OJyIifdaowlJUVIQXXngB+fn5sLGxgZ+fHw4cOIBRo0YBAObNmweVSoW33noLpaWl8Pf3x8GDB+Hu7l6fcenSJVy7dq3+64EDB2L79u0IDw/Hhx9+CDc3N3z55Zd49tlnJdpEup8U1RqEb0vA3sQCPDfEFYvH+0g6/l6p0iB8WyL2JOTjmcGueP8f0uYTEZF+avYcFn3BOSytLzbrOkIj5FCqNPhkih/G9ekiXXjGr4gzG4DQyFiUVWrw8RQ/jPeTMJ+IiFpFi510S/R3Op3Aut8uY/X/UtHH2QZRrw6Bi20HSfP3J+Zhbszv8O1mg4iXpc0nIiL9x8JCzVJcrsb8zXE4nn4Ncx51x/xRPWEi4fj7axVqLNgcD2Tk4OWHB2HBaGnziYiobWBhaa/KsgCt5sbXZjLAyr7uz+pyoKLo1ucYGtV9dLj+9revDYyAv1wX6nh6Md7aFA9AYOOsQXikp72km3Ay4xrmbYqDEAI/jO6JPo96SZpPRERtBwtLe5UTAzj63phdcj0TKL4IGJkAtWqg498mAwtRV3A0qrqPEOs0dbNPdLU33wRQq9Nhe2wODiZlwfuhsVg93R8O1rcf6NYUtVodvvw1HV8fzcBQdzt8/pQ/HArufokHIiJq31hY2qseDwOZxwHviYCRMdDZHVDm1ZUVW7cmx+Zcr8LcSDnic5zw5QAzjH+iPwyNpRvUlltWjbBIOeTZZXh7dC/MGeYOQ0MDoECylyAiojaIhaW9qioFOvWoKyt/knVtVuS+xHws2poAa3MTbH4tEAG4AFxLBUwsmhb455A3IQBDIxxNK8FH+9LQwcwE217oUze+v6oYEDqgpqJZ605ERG0bC0t7pcgGHhouSZRKo8U/96Tgp9NZGOvrhI+n+MHGwgTQDgKUuX+8VaQDDBp5Muwfn6hX1+rw7dE07IrLxiMenTF/pAesTdV12QaGAAwAO09JtoWIiNomFpb2ytCo7pwVJ99mxWQUlSMkQo4r1yrx0SRfPDPI9cb4eyNjoFPzLqFwqbgCIVFyXCoGlkwcg+cGu3K8PhER3YKFpT0qTgPMOzarrAghsOVcDpbuTEa3ThbYETIUXk7SDeQTQmBrbC7e35GELjbm2PHmUHh34cA/IiK6PRaW9kanBUoyAK9xTY4oV2nw/7YnYWd8Hp4a4IKlT/igg6l03yoV6los+SUJ2+W5mD7AGR880VvSfCIian/4W6K9MTAEtDVNfnpCThlCI+UoqajBmhl9MbFvNwlXDkjKVSAkIhbF5eoWySciovaJhaU90NYC5XmAsTlQkAiYy+qGw5k1/ArGOp3A+pNX8Mn+i/DuIsPGWYPQvbOlZKsohMAPJzOxct8FeDnJ8ONLg9DDTrp8IiJq31hY2oOSjLpP1NSq6kpK0UXArmeDC0tJhRpvb4nHkdRivPKwGxaO8YKpsXTj70sra/BOdDx+vVCE2cFuWPS4tPlERNT+sbC0VdcygDw54NQHKEkHeo0Hrl8B0v8HuA0DbJwbFHPqUgnmbZJDoxX4YeZADPdykHQ1z1wuQVhUHNS1WqyfOQCPeTlKmk9ERA8GFpa2qKIYUGQBHiOAK7/9cc0fw7pptp3nNCiiVqvDV4czsPZwOoa4dcaXM/rCUSbdeH2tTmDt4XR8dSgdA3vYYs2MfnCykS6fiIgeLCwsbZGhEVBTCXSwBR4aBlh0atTT8xXVCIuMw7mrpZg/sifeGO4BI0PpZp8UKFQIi5IjJrMUYSN6IuQxafOJiOjBw8LSFmWdApwH1v25kWXlYEohFkbHw8LECFGvBmKQm62kq3b4YiEWbI6HmbERIl8ZgsEPdZY0n4iIHkwsLG2SQf1Y+4ZS12qxcu9F/Ph7JkZ6O2LVVD90sjSVbI3UtVp8uj8V35+4gpHeDlg11V/SfCIierCxsLQlQgDX0gDH3nUn3KrKAAfvez7tcnEFQiPlSC+swAcTfPBiUA9Jx99nXqtEaKQcqQXlWDrBBzMlziciImJhaUtyY+uucKyuALr2BTTV93zKttgcLP4lCY4yc2x7Iwi+3WwkXaUdcbl4b1si7K3NWiSfiIgIYGFpW9RKwMEHsL73R4Mr1bVYsiMJ22JzMbl/N3w40RdWZtL9766qqcXSHcnYcj4Hk/p1w/Inpc0nIiL6K/6GaSuuZQC6WiDnLOA94a6LJucpEBohR4FShdXT/DEloGEzWRoqJU+J0MhY5JWp8Nk0f0yVOJ+IiOjvWFjaiurrgKMvIOtyx0WEENh46io+2nMBHg5W2B0ajIfsrSRbBSEEfjp9Fcv3XIC7vRV2zw2Gu4T5REREd8LC0lZ06g5cz7xjYSmrqsHC6AQcTCnEzKAeCB/nBTNjI8leXlGlwTtb43EguRAvBnZH+DhvmJtIl09ERHQ3LCxthZVD3VRbl0G3PBSTWYqwSDmqNFp893wARvd2kvSlz18txdzIOFSoa7Hu+QCMkTifiIjoXlhY2gpFDmDnedNdWp3AN0cy8MWvaQjo3glrZvRD144Wkr2kTifw7bFL+PxgGvq5dMSap/uhm4T5REREDcXC0lYUJt+YbgugUKnCvKg4nL5SgtDhHpg7whPGRtJdAbmoXIX5m+Jx8tI1hAz3QJjE+URERI3BwtJWaGuAxGhg4GwcSS/Bgs3xMDY0wM8vD0aQu52kL3UsrRgLNsfBwMAAP88ejCAPafOJiIgai4WlrZB1g0bWHZ/uS8V/jl/Bo73ssXqaPzpbmUn2EhqtDp/9LxXrjl3GsJ72WD3dH3YS5hMRETUVC0sbkW3QFT9FbsSPpb5YPN4bs4a6wVDCKyBnl1YhNFKOpFwF3hvnhZeDH5I0n4iIqDlYWNqAXfF52L/tR1RYdEX060Hwd+koaf6ehHy8uzUBHS1NED0nCH0lziciImouFhY9Vl2jxbJdyYiKycYE/7H41yRfWJubSJr/4e4URJ7Nwj/8umDF5D6QSZhPREQkFRYWPXWxQImQCDlyrlfh0yl+mDbAWdIrIKcVliMkIhZZpVX4eHIfPDXQhVdYJiIivcXComeEEIg4m4UPd6XAzc4Su0KC4eloLWl+VEw2lu1KhqttB+wMCUZPCfOJiIhaAguLHlFUaxC+LQF7Ewvw3BBXLB7vI+n4e6VKg/e2JWJ3Qj6eGeyK9/8hbT4REVFLYWHRE7FZ1xEaIYdSpcE3z/bHuD53vshhU8RllyE0MhZllRp8/Ux/jPeTNp+IiKglsbC0Mp1OYN1vl7H6f6no42yDqFeHwMW2g6T5/z1xGZ/uT4VvNxtEvCxtPhER0f3AwtKKisvVmL85DsfTr2HOo+6YP6onTCQcf3+tQo0Fm+NxLK0Yrw9zx4LR0uYTERHdLywsreR4ejHe2hQPQGDjrEF4pKe9pPknM65h3qY4CCGwYdYgDJM4n4iI6H5iYbnPNFodPj+Yhn8fu4RgDzusnu4PB2tzyfJrtTp8+Ws6vj6agaHudvj8KWnziYiIWgMLy32UXVqFsCg54nMUeGeMF157RNrx97ll1QiLlEOeXYa3R/fCnGHuHK9PRETtAgvLfbIvMR+LtibA2twEm18LRED3TpLmH0guwDvRCbAyM8bm14YgoLutpPlEREStiYWlhak0WvxzTwp+Op2Fsb5O+HiKH2wspBt/r9JosXLvBWw4dRWP93bCJ1P8YNOB4/WJiKh9YWFpQRlF5QiJkOPKtUp8NMkXzwxylXT8/aXiCoREyHGpuALLn/TFc4OlzSciItIXLCwtQAiBLedysHRnMrp1ssCOkKHwcpJJmr81Nhfv70hCFxtz7HhzKLy7SJdPRESkb1hYJFau0uD/bU/Czvg8PDXABUuf8EEHU+l2c4W6Fkt+ScJ2eS6mD3DGB0/0ljSfiIhIH/E3nYQScsoQGilHSUUN1szoi4l9u0man5SrQEhELIrL1S2ST0REpK9YWCSg0wmsP3kFn+y/CO8uMmycNQjdO1tKli+EwA8nM7Fy3wV4Ocnw40uD0MNOunwiIiJ9x8LSTCUVary9JR5HUovxysNuWDjGC6bG0o2/L62swTvR8fj1QhFmB7th0ePS5hMREbUFLCzNcOpSCeZtkkOjFfhh5kAM93KQNP/M5RKERcVBXavF+pkD8JiXo6T5REREbQULSxPUanX46nAG1h5OxxC3zvhyRl84yqQbf6/VCfzrcAbWHErDwB62WDOjH5xsOF6fiIgeXCwsjZSvqEZYZBzOXS3F/JE98cZwDxhJOP6+QKFCWJQcMZmlCBvREyGPSZtPRETUFrGwNMLhi4WYvzkeFiZGiHo1EIPcpB1/f/hiIRZsjoeZsREiXxmCwQ91ljSfiIiorWJhaaBf5Ll4a3McHuvlgM+m+aOTpalk2epaLT7dn4rvT1zBSG8HrJoqbT4REVFbx8LSAKkF5XgnOgGT+zlj1VQ/Sa+AnHmtEqGRcqQWlGPpBB/MDOrB8fpERER/w8LSACv3XYCzrQVWTPaVtKzsiMvFe9sSYW9thm1vBMG3m41k2URERO0JC8s95FyvwtHUYqye5g8zYyNJMqtqarF0RzK2nM/BpH7dsPxJX1iZ8X8FERHRnfC35D38fqkEBgbAqN7SzEBJyVMiNDIWeWUqfDbNH1MDnCXJJSIias9YWO7hakklusjMITM3aVaOEAI/nb6K5XsuwN3eCrvnBsPd3kqitSQiImrfWFjuoVYrmj0KX1GlwaKtCdifXIAXA7sjfJw3zE2keXuJiIjoQcDCcg/21mYoUKpQq9XB2KjxxeX81VLMjYxDhboW654PwJjeTi2wlkRERO0br6J3D/1cO0Gl0SE2q6xRz9PpBL4+koHp606ji4059oY9zLJCRETURI0qLN9++y38/Pwgk8kgk8kQGBiIffv23bTMqVOn8Nhjj8HS0hIymQyPPPIIqqurG5T/8ccfw8DAAPPmzWvMarWofi4d4WrbAetPXGnwc4rKVXhh/Vl89r9UvPGoO6JeHYJuHS1acC2JiIjat0YVFmdnZ3z88cc4f/48zp07h8ceewwTJ05EcnIygLqy8vjjj2P06NE4e/YsYmJiEBISAkPDe79MTEwM1q1bBz8/v6ZtSQsxNDTAW6M8sT+5ALsT8u65/LG0YoxbcxypheX4efZgLBjdq0lvJREREdENBkII0ZwAW1tbrFq1CrNnz8aQIUMwatQoLF++vFEZFRUV6N+/P7755hv885//RN++ffHll182KkOpVMLGxgYKhQIymaxRz70XIQTCouKwP6kAKyf3weT+3W6ZRluprkXvpQcAAMN62mP1dH/YWZlJuh5ERETtTUN/fzf5n/5arRZRUVGorKxEYGAgioqKcObMGTg4OCAoKAiOjo4YNmwYTpw4cc+sN998E+PHj8fIkSMb/PpqtRpKpfKmW0sxMDDAZ9P88UTfrliwJR5T/30KP52+itOXS3A8vRj/OpyOgH8eBAAEdO+EH2YOZFkhIiKSUKM/JZSYmIjAwECoVCpYWVlh+/bt8PHxwenTpwEAH3zwAT777DP07dsXGzduxIgRI5CUlARPT8/b5kVFRSE2NhYxMTGNWo+VK1di2bJljV39JjM1NsRn0/wx3q8Lvjt2GUt3JkOrqzs41cHUCGN6O2F2sBv8nDvet3UiIiJ6UDT6LaGamhpkZWVBoVAgOjoa//3vf3Hs2DGUlZVh6NChCA8Px4oVK+qX9/Pzw/jx47Fy5cpbsrKzszFgwAAcPHiw/tyVRx99tEFvCanVaqjV6vqvlUolXFxcWuQtoduprtEiT1ENE0NDdO1ozvNUiIiImqChbwk1+giLqakpPDw8AAABAQGIiYnBmjVr8O677wIAfHx8blre29sbWVlZt806f/48ioqK0L9///r7tFotfvvtN/zrX/+CWq2GkdHtB6yZmZnBzKz13naxMDXipFoiIqL7pNmD43Q6HdRqNXr06IGuXbsiNTX1psfT0tIwduzY2z53xIgRSExMvOm+l156CV5eXli0aNEdywoRERE9WBpVWMLDwzF27Fi4urqivLwcEREROHr0KA4cOAADAwMsXLgQS5cuhb+/P/r27YsNGzbg4sWLiI6Ors8YMWIEJk2ahJCQEFhbW8PX1/em17C0tETnzp1vuZ+IiIgeXI0qLEVFRXjhhReQn58PGxsb+Pn54cCBAxg1ahQAYN68eVCpVHjrrbdQWloKf39/HDx4EO7u7vUZly5dwrVr16TdCiIiImrXmj2HRV+05BwWIiIiahktPoeFiIiI6H5hYSEiIiK9x8JCREREeo+FhYiIiPQeCwsRERHpPRYWIiIi0nssLERERKT3WFiIiIhI77GwEBERkd5r9sUP9cWfA3uVSmUrrwkRERE11J+/t+81eL/dFJby8nIAgIuLSyuvCRERETVWeXk5bGxs7vh4u7mWkE6nQ15eHqytrWFgYNDaq3NfKZVKuLi4IDs7+4G/jhL3xQ3cFzdwX9yM++MG7osbWmtfCCFQXl6Orl27wtDwzmeqtJsjLIaGhnB2dm7t1WhVMpnsgf8L9yfuixu4L27gvrgZ98cN3Bc3tMa+uNuRlT/xpFsiIiLSeywsREREpPdYWNoBMzMzLF26FGZmZq29Kq2O++IG7osbuC9uxv1xA/fFDfq+L9rNSbdERETUfvEICxEREek9FhYiIiLSeywsREREpPdYWIiIiEjvsbC0AbGxsRg1ahQ6duyIzp0749VXX0VFRcVNy2RlZWH8+PHo0KEDHBwcsHDhQtTW1t4xMzMzE7Nnz4abmxssLCzg7u6OpUuXoqampqU3p1laYl8AQGlpKZ599lnIZDJ07NgRs2fPviVX36SlpWHixImws7ODTCZDcHAwjhw5ctMyhw4dQlBQEKytreHk5IRFixbdc18UFBTg+eefh5OTEywtLdG/f39s3bq1JTdFEi21PwDg1KlTeOyxx2BpaQmZTIZHHnkE1dXVLbUpzdaS+wKom0w6duxYGBgY4JdffmmBLZBOS+yL0tJShIaGolevXrCwsICrqyvmzp0LhULR0pvTbC31vaFSqfDmm2+ic+fOsLKywpQpU1BYWCjtygvSa7m5uaJTp07i9ddfFxcvXhRnz54VQUFBYsqUKfXL1NbWCl9fXzFy5Eghl8vF3r17hZ2dnQgPD79j7r59+8TMmTPFgQMHxKVLl8SOHTuEg4ODWLBgwf3YrCZpqX0hhBCPP/648Pf3F6dPnxbHjx8XHh4e4umnn27pTWoWT09PMW7cOBEfHy/S0tLEG2+8ITp06CDy8/OFEELExcUJU1NTsWzZMpGeni6OHj0qvLy87vn/eNSoUWLgwIHizJkz4tKlS2L58uXC0NBQxMbG3o/NarKW2h+///67kMlkYuXKlSIpKUlcvHhRbNq0SahUqvuxWU3SUvviT59//rkYO3asACC2b9/eglvSfC2xLxITE8XkyZPFzp07RUZGhjh06JDw9PS86WeRvmqp743XX39duLi4iEOHDolz586JIUOGiKCgIEnXnYVFz61bt044ODgIrVZbf19CQoIAINLT04UQQuzdu1cYGhqKgoKC+mW+/fZbIZPJhFqtbvBrffrpp8LNzU26lZdYS+2LlJQUAUDExMTU37dv3z5hYGAgcnNzW2hrmqe4uFgAEL/99lv9fUqlUgAQBw8eFEIIER4eLgYMGHDT83bu3CnMzc2FUqm8Y7alpaXYuHHjTffZ2tqK//znPxJugbRacn8MHjxYLF68uGVWvAW05L4QQgi5XC66desm8vPz9b6wtPS++KvNmzcLU1NTodFopFn5FtBS+6OsrEyYmJiILVu21N934cIFAUCcOnVKsvXnW0J6Tq1Ww9TU9KYLQllYWAAATpw4AaDucHWfPn3g6OhYv8yYMWOgVCqRnJzc4NdSKBSwtbWVaM2l11L74tSpU+jYsSMGDBhQf9/IkSNhaGiIM2fOtMSmNFvnzp3Rq1cvbNy4EZWVlaitrcW6devg4OCAgIAAAHX7y9zc/KbnWVhYQKVS4fz583fMDgoKwqZNm1BaWgqdToeoqCioVCo8+uijLblJzdJS+6OoqAhnzpyBg4MDgoKC4OjoiGHDhtV/v+mjlvzeqKqqwjPPPIOvv/4aTk5OLbodUmjJffF3CoUCMpkMxsb6e4m+ltof58+fh0ajwciRI+vv8/LygqurK06dOiXdBkhWfahFJCUlCWNjY/Hpp58KtVotSktLxZQpUwQAsWLFCiGEEK+88ooYPXr0Tc+rrKwUAMTevXsb9Drp6elCJpOJ7777TvJtkEpL7YuPPvpI9OzZ85b77e3txTfffCP9hkgkOztbBAQECAMDA2FkZCS6dOly09s2Bw4cEIaGhiIiIkLU1taKnJwc8fDDDwsAIiIi4o65169fF6NHjxYAhLGxsZDJZOLAgQP3Y5OapSX2x6lTpwQAYWtrK9avXy9iY2PFvHnzhKmpqUhLS7tfm9ZoLfW98eqrr4rZs2fXfw09P8IiRMvti78qLi4Wrq6u4r333mupzZBMS+yPn3/+WZiamt5y/8CBA8U777wj2brzCEsreffdd2FgYHDX28WLF9G7d29s2LABq1evRocOHeDk5AQ3Nzc4Ojre9TLcjZGbm4vHH38c06ZNwyuvvCJJZmPo075obQ3dF0IIvPnmm3BwcMDx48dx9uxZPPnkk5gwYQLy8/MBAKNHj8aqVavw+uuvw8zMDD179sS4ceMA4K77a8mSJSgrK8Ovv/6Kc+fOYf78+Zg+fToSExPvyz74q9beHzqdDgDw2muv4aWXXkK/fv3wxRdfoFevXli/fv392Ql/aO19sXPnThw+fBhffvnl/drkO2rtffFXSqUS48ePh4+PDz744IOW3Ow70qf90aIkqz7UKEVFReLChQt3vf39nIuCggJRXl4uKioqhKGhodi8ebMQQoglS5YIf3//m5a9fPmyAHDPEyVzc3OFp6eneP755286N+R+au198f3334uOHTvedJ9GoxFGRkZi27Zt0m1oAzR0X/z666/C0NBQKBSKm57v4eEhVq5cedN9Op1O5Obmiqqqqvrzdc6ePXvb18/IyBAARFJS0k33jxgxQrz22mvSbmwDtPb++PN75//+7/9uun/69OnimWeekXZj76G190VYWFj9v8r/vAEQhoaGYtiwYS212bfV2vviT0qlUgQGBooRI0aI6upqybezoVp7fxw6dEgAENevX7/pfldXV/H5559Ltp36+2ZbO2dvbw97e/tGPefP8zLWr18Pc3NzjBo1CgAQGBiIjz76CEVFRXBwcAAAHDx4EDKZDD4+PnfMy83NxfDhwxEQEIAffvih1dpza++LwMBAlJWV4fz58/Xv4x4+fBg6nQ6DBw9u6mY1SUP3RVVVFYBb/8VjaGhYf1TgTwYGBujatSsAIDIyEi4uLujfv3+jco2MjG7JvR9ae3/06NEDXbt2RWpq6k33p6WlYezYsQ3eDim09r5499138fLLL990X58+ffDFF19gwoQJDd4OKbT2vgDqjqyMGTMGZmZm2Llz5y3nfdxPrb0/AgICYGJigkOHDmHKlCkAgNTUVGRlZSEwMLDR23NHklUfajFr164V58+fF6mpqeJf//qXsLCwEGvWrKl//M+P8o4ePVrExcWJ/fv3C3t7+5s+ynvmzBnRq1cvkZOTI4QQIicnR3h4eIgRI0aInJwckZ+fX3/TZy2xL4So+1hzv379xJkzZ8SJEyeEp6enXn+subi4WHTu3FlMnjxZxMXFidTUVPH2228LExMTERcXV7/cp59+KhISEkRSUpL48MMPhYmJyU3nHOTk5IhevXqJM2fOCCGEqKmpER4eHuLhhx8WZ86cERkZGeKzzz4TBgYGYs+ePfd7MxuspfaHEEJ88cUXQiaTiS1btoj09HSxePFiYW5uLjIyMu7nJjZYS+6Lv4Oen8PSUvtCoVCIwYMHiz59+oiMjIybfn7W1tbe781ssJb83nj99deFq6urOHz4sDh37pwIDAwUgYGBkq4/C0sb8PzzzwtbW1thamoq/Pz8bvnIqRBCZGZmirFjxwoLCwthZ2cnFixYcNPH644cOSIAiCtXrgghhPjhhx8EgNve9FlL7AshhCgpKRFPP/20sLKyEjKZTLz00kuivLz8fmxSk8XExIjRo0cLW1tbYW1tLYYMGXLLicXDhw8XNjY2wtzcXAwePPiWx69cuSIAiCNHjtTfl5aWJiZPniwcHBxEhw4d7rif9U1L7Q8hhFi5cqVwdnYWHTp0EIGBgeL48eMtvTnN0pL74q/0vbAI0TL74s+fIbe7/fXnij5qqe+N6upq8cYbb4hOnTqJDh06iEmTJkn+D2ADIYSQ7ngNERERkfTax0criIiIqF1jYSEiIiK9x8JCREREeo+FhYiIiPQeCwsRERHpPRYWIiIi0nssLERERKT3WFiIiIhI77GwEBERkd5jYSEiIiK9x8JCREREeo+FhYiIiPTe/wfy2yQ1NoS01wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 1 out of 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "performing squish no 2 for state MO\n",
      "clipPoly 1 intersects [1, 2, 10, 37, 43, 73, 82, 112] counties and a total of 62 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 2 out of 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    }
   ],
   "source": [
    "#starting with MN, we moved the \"option2\" block earlier in the notebook, as we run it before running option 1 (as in other states)\n",
    "print(\"for many states we move in outcroppings via clipPoly's before running the wedgePoly solver\")\n",
    "vtdCP = tractCP.copy()\n",
    "#CODE TO SQUISH GEOMETRIES FROM CLIP LINES INTO THE MAP\n",
    "print(\"adding code here to 'push in' state panhandles ...\")\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "#Adding in here trimming ops\n",
    "toSquishUnitsList = list()\n",
    "toSquishGeomsList = list()  #combined list of geoms we will squish (over all squish operations)\n",
    "toSquishCountiesList = list()\n",
    "squishedShapesList = list()   #unit shapes after squishing, list over all squish operations\n",
    "if nClips > 0:\n",
    "    clipPoints = ast.literal_eval(trimDF[\"clipPoints\"][DFrow])   #sets of four points\n",
    "    farPoints  = ast.literal_eval(trimDF[\"farPoints\"][DFrow])    #pairs of points\n",
    "    newFarPoints  = ast.literal_eval(trimDF[\"newFarPoints\"][DFrow])   #pairs\n",
    "    cPts = [Point(clipPoints[i][0],clipPoints[i][1]) for i in range(len(clipPoints)) ]\n",
    "    fPts = [Point(farPoints[i][0],farPoints[i][1]) for i in range(len(farPoints)) ]\n",
    "    nfPts = [Point(newFarPoints[i][0],newFarPoints[i][1]) for i in range(len(newFarPoints)) ]\n",
    "    \n",
    "    for clipNo in range(nClips):\n",
    "        print(\"performing squish no\",clipNo+1,\"for state\",STATE)\n",
    "        n0,n1,n2,n3 = int(4*clipNo), int(4*clipNo+1), int(4*clipNo+2), int(4*clipNo+3)\n",
    "        clipPoly = Polygon([cPts[n0],cPts[n1],cPts[n2],cPts[n3] ] )\n",
    "        cutLine = LineString([cPts[n0],cPts[n1]] )\n",
    "        farLine = LineString([fPts[int(2*clipNo)],fPts[int(2*clipNo+1)] ] )\n",
    "        newFarLine = LineString([nfPts[int(2*clipNo)],nfPts[int(2*clipNo+1)] ])\n",
    "        \n",
    "        toSquishCounties = list()\n",
    "        toSquishUnits = list()\n",
    "        for c in range(nCounties):\n",
    "            if clipPoly.intersects(countyGeom[c]):\n",
    "                toSquishCounties.append(c)\n",
    "                if clipPoly.contains(countyGeom[c]):\n",
    "                    toSquishUnits = toSquishUnits + countyTractList[c]\n",
    "                else:\n",
    "                    for t in countyTractList[c]:\n",
    "                        if clipPoly.contains(vtdCP[t]):\n",
    "                            toSquishUnits.append(t)\n",
    "        print(\"clipPoly\",clipNo,\"intersects\",toSquishCounties,\"counties and a total of\",len(toSquishUnits),\"units\")\n",
    "        toSquishGeomUnion = vtdGeom[toSquishUnits[0]]\n",
    "        toSquishGeoms = list()\n",
    "        for t in toSquishUnits:\n",
    "            toSquishGeomUnion = toSquishGeomUnion.union(vtdGeom[t])\n",
    "            toSquishGeoms.append(vtdGeom[t])\n",
    "        unclippedGeom = MAP.difference(toSquishGeomUnion)\n",
    "        altCutLine = toSquishGeomUnion.buffer(0.001).intersection(unclippedGeom)\n",
    "        print(\"Here is the original cutLine and an alternate based on cut shapes\")\n",
    "        #plotPoly(MAP,0.1)\n",
    "        plotPoly(cutLine.buffer(0.02))\n",
    "        plotPoly(altCutLine,0.1)\n",
    "        plt.show()\n",
    "        # could use altCutLine for a more accurate squish at the squish-no_squish boundary ...\n",
    "        #newShapes = squish(clippedGeomList, altCutLine, farLine, newFarLine)\n",
    "        squishedShapes = squish(toSquishGeoms, cutLine, farLine, newFarLine) #..but may distort results\n",
    "        toSquishUnitsList.append(toSquishUnits)\n",
    "        toSquishGeomsList.append(toSquishGeoms)\n",
    "        toSquishCountiesList.append(toSquishCounties)\n",
    "        squishedShapesList.append(squishedShapes)\n",
    "        for geo in toSquishGeoms:\n",
    "            plotPoly(geo,0.1)\n",
    "            plotPoly(geo.centroid.buffer(0.004),0.1)\n",
    "        for geo in squishedShapes:\n",
    "            plotPoly(geo)\n",
    "            plotPoly(geo.centroid.buffer(0.002),0.4)\n",
    "        print(\"These are your orig (faint) and squished shapes for clip no\",clipNo+1,\"out of\",nClips)\n",
    "        plt.show()\n",
    "        pause = input(\"enter 1 when ready\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "62667c48-6c66-4381-b01d-518af61651bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now apply the squish to all impacted vtds.  \"tractGeom\" will retain original vtd shapes, so we can reset if needed\n",
    "for i, vList in enumerate(toSquishUnitsList):\n",
    "    squishedShapes = squishedShapesList[i]\n",
    "    for j, v in enumerate(vList):\n",
    "        vtdGeom[v] = squishedShapes[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7b967d22-41fb-454f-ae47-d72d712daa4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rVav4U+DrvG4cgjCTiylHaUaWbOaGkbfTzWE3HjwUE+D44Z+7A+yOCuriQy9X1TdwcXMLMon/7gI1mnNjv+bMVj/D7OQiSQKnFqV3wF4ddtgQVZz0tcEMOTL7jvudB59j2ev3c+rVf2Dk9OSh1Wsffpu81QH63ngIhmkSjdkMmruSm/wFnDWpP01hiyOWr6IftfxO/pAaYxTnHfY8htHdynHt5bOoaAhiCIUhNFJoDOkORRpSY0iBVecgpMRv+ymL+dE+nzv5g+hAAB0IMuSj13nhnG9w95HH8fSkYRxRkp/19Xp4/F/mi+i/PUvKf4v2uh6LtpnDOG6y7T716ipQfWlr38tb4WUUxPYwrPRQHKVxUDiOQ1OknXwpaNCf4BhRdhw0i3G7m/EHrc4w3XgStwTWNE14aj2G0YxtD3KdL+M5VNyuWQASy15De3he2kuYu6mZsCWZPjDhbBnv2uOCaG+tIrw7dTKtyZXlDFyxg+2BAFMHTYhv1rmPhHB423qLfWY9x5cdT7URIr+wxM3NEj/fhJwRm1fRp/kN1h83leKSvZSSiw/XZcOtK9NZX0ZrTWxvlAZRT/mwEa7FR9OR+E7HRQ3Et3FniLZFiNZvIxizKKksIKFguoqc3fUzMUuaMStzEKabQ6ZfvgW12xnQdwjFgSgf5w9kbut4Jo4tw/TJxO1zjx0/7rLlNTTsK6JywoRuQoj4/dEaVsQaOTK/EhGMsWrPEo5va+fIiWfS7gu6CUGkgZYGexqjjFj8DzYHRvDnk0ykVqjaXHJClRimxIwno/OZEikFPLOJXX0lE47p3+2wifmBy5rYMLuOvBR5XlLRf9Qwlr0OkdZw6kaORmmFEfcFaYk7GD8ZaeOSvACFeQFmhDazNWrQkHs0fVrf5aX5Z3P6oS90G34qbvFj5hkccchkVCzSkfJf2fFXx6HNbGBLM7SYUOgvQFgWIhrFaGnBsKKYsSh7QsX4+g5nSkEO5yzfxMCgn4FBP/mmwbeqyjis2BMtHh7/LTyR8t9Ca3485kO48BlueHQOAEWDxsPxX+3erGk7PH8y1w86leMO/2XSXT2z7HfcsPxpLv/FZfQtHJ3x0PMX/A6n6VGOnfUU/hRJ0d544yJsZ33GfY3q43DnN7+ddN0Vr6/i5Q+2ptzWNAy+W1DGddFmjrnoZGQKs/vsJz5kcXg5N59yY9pz2ZjzARZvMPC0o5k862sZzz3vlhuZu/gTLr3r3oxtE7Tu3sW9P/wO42bNYNJlFyZts++BMprW51B5wQGY8Q6sP9A1l+nTz6+hef5mHvvKRPLyklfhvbXxPl7YEWTY0c+lPJ+OwaBVL8CKN9354+/u0a4A2LT4VQ5yVlI88kBKS0rTXueWZzbSVFpAaPzgpOtDDbuAurTDecnYt9N16pVpHG61o9yyC4lj+VyLzyfS4YhXl3Jufj6G4Sdg+jl7+n08suAH9Gt7lbeWX8NxB/wJcIfAtNAUzJjC5EuuT3tON/7sBqx91Zz+z/t6rNvZEuaw37/LxeNG8fzkobxd18zCpjaWt4R5bV8ThabhiRQPj/8in388oUc33qtr5piFa7liawsqbr2YOnUqFRUVjBw5skf7RL4MKVL7L7S0rALAUelTgCdQjkYIMNNFLQjRbSAjGZm6JyMLH93OKKU0bbIMwdGOE99ndu2r9+zuvXdtNu0TfkEpHDK77yZNBBLZZWXpcV6LH07axH/uA1SoGlruPILG3VvS7s7BLRqY8nDxX4pMLlD7o2z3M6oaMyRlG21rVBe/n1y/yUNl5ZwbNVkfEvzObuXN6DDMqBsNd+G0v7JZjIS65/h404Pu+SsHqQSGL/2wlXsNKp6Gtie1YdcXrDjowy8lJ/Up4vrh/TmhzLUgtTrp/ZM8PDw+XzyR8gVzy5YaVrVGeKYhRrOZC0Jw6qmn8t3vfpdJkyb1aN+Z2TN1Z1VU6GamNYzsys2r+D5TWS4gLgwyhHhmQsaHbFSakOdO58/UxxKCbk/WqUg4kKbP5NqJ42Qn6rqRCL1Nc2u0k0V47uedC23saTDyBHf+pR/AMz2tWwPGTKfunJfIUW3UPHgBkfbWlLuzAeWk+dwSieR66YSduOf7RxR1QynUfp/38RP6cccJ41kzeRQHtGkOaLA5uqbzb+Prhz9DjS6mdsvvWbnrbSxlYTjZihQ7pfNsXTgGQHGwu6A/p9LN1rsq3bCVh4fH544nUr5AIo5iWUt7x/tHdt/P5g3pe6mOH+s0IiUhTlTWj7XZhCHLLNqk72OlzJx1tiOfStrOTmQVNa1VwpKSnUgZUFmVdYr93qCj8YtKZ0mJv6YTZw4ic0h5V772JEyIDxeueBru7lm3aPDYg1gz7oeMjq1i3cdv9FifwBag01hSpPEpRYrlihQzjUhR4SgSSe3fXmbvP15j3wNvUvfIu9Q/ORveWcKTKsZ9C1s5t6apY5ugmcNph76EhcGetZexYvtLbrHGbC0pKYRtczz7bZ6/u0gp8pkUmwa7oxZ3bNnF7E9WEImkydDr4eHxueD5pHyB1Md/oP2WZvy2KNLxU7fXZrC1XzKzLoIkGnNzQdTGWrDjwzndwmY1OPGOznFiOI6brVNKmUbYZPn4nqH/ydS/JwSIrTRmiv7aiJ+j8zmERXckGEsXety1/Wcx1ac5hNNixpukH8rJtB/dNRopq3MScPg1sOLfAKj2OlrbGlDKdbTW2kFpxdADj4DVsGnnDuq21eFot9SAozSOBlspBmmNY6UWKe3RdlrztrBkJRQ3VXc4IydeldK0NdQSUhqJa03TjmLjwvcIlkTYuPofrpjUIh4WL9AIBJK90SUEQzOJbqtIUrtIATEEBqp6Z7c1RTl9GTziRuo3/YzWbT9nwJGFCF8Wz12Ok9KSEok77YaS7Oe+8YN5eFcdT3+8lIY1CwmdeioHTZ2a+XgeHh6fGk+kfAH84dU1rK1pYUhZLquOHsOTP5nbsS5vxd3ITWen3DZqmjCgHzdte4GbHnkh7XGWLj6dDQYIrd1kZrpzWEJo4YakakFUGqmSbHagogoz2MzCV87vrHaMAYmKyBhY9jBwUjsNGvGDpEtjnxg2UL3Mt5GUmOs/kO0oVTjix5d/Bv+46j+u4NJuxIzWwu08EZ3L4/5DaM2or7ZQm3877756W4fI0IkAKQlMA4Rg3RyJ0NJdYSsKn/GRs8APQjBSa54G1r366/j2nc45Ii5OjtWaAeWDgG9kfQs+euVGDo3Py7ZaCm4dnLLt3rUfc/XK5HlYniCXuljq4bA9zbsI5+1g4ZYdkMa9JbhrC76m7pFsI8/cTU3TX1JvdDBsqHufE85ehYrG0PHJnY+iIzG2/OIydKkAru626eRBX6WlfCZvf3QtpaNnU7lIsXnOW/GQdo1GuSJfuGI/7FiU1NdRV1iU9FQicWtSMInKPqw4n8OK87l952aagHfffpu5H37IkCFD2LBhA1VVVRx11FFUVFSkvlYPD49e4YmUL4B7Z7vptT9Yv5dRlflccc/R6HATDQ9cwIqVO8mf/P3OJ+r9OvT2pga+un4Bjf0nM3DEBJRWbuBtvFMTQqC1Q77YQ3luH8xPHqEvNURy++IfPA2tbLS20NpGKwulLar3NbKhsZx0RPeOQOXuoDhPobSF1g7adadE46BRSHscRji1SOkYykknUqTbCVQvf44cn447CsdfNWgc9kW3ss3cxTVv3IvEADSOtnGUg6MdbO2glKL/vNmcDcR+8nu2TH+MvIAJWrlDEjoxKdDu0769dyhGycH4/HUI6T5MCwFCuong3PciHskbD82VMUIlOxGxIvpZh8StJYnw7bgg0RKtCzDLSt37phU7o39HDQtRUP41UJqwbbO+LYLI8yOFe68kgJQI6aaLL/ngaSY1bUz7Oe3PVgsOBV4c9z369h2NkKa7P2G4KeiFgRCS9qgiJzCaG/yFGFJgSIFPiI559cQGyktDKY8zoKoSPoYTph/PgLHD3OKFUrp+SFLgWFHueeAeCsr78o1b7kIaBtKUSEPy7htTUPWTmXzwza71S7jWPzfNvWLz+z8i4t+BEAIjGIBgz+gnpVoQgaKk55Yf6sPM4T9jyabZRPLbIc8C5Vp5ur46UU1ZSyl28XgmH9fTHwwgGnf0zfGlHroLBvw04TqiNzY2snTpUgDWrl1LIBDgjDPOSLmth4dH7/BEyhfA6Mp81ta4qd/PnlqFUjbrt93KvpHFbNgW4civ/T7ltlvXrSH319dw/MyvMv3YEzIeq+7dByi1IrR9/2Vyy6qSttnz2C3s3d2Ydj922yB2V5/MGb/6Uco2oUVvE0hTyTfhk2KnsWxEjW1AERsivyWUYkx/S3QKBOD1mrs6lmndKQhAgpYMLHI4Gwi0tmHN/YRG6bbpSAMDHYntBOAvKUMU21x861fJluaGLSxcCqGcgxh9wl2ZN4iz+9n7MCv60P+6zif/4Rm2qT377zRv7Z2bWNWIw2HXi+QMP5qDJh+btu0hadbN9m3El+ZzMw33p6K8rJj+g3oKXqUc0IqyQYPJ2y9E15cXRkailFUlvwM7tElMZbjumIPwp/7u+XPdKuF9Dx1Pv0knJW1Tva0R/fcVzDjjTKYdNThpm0jceTiUxgnasm00cPXVV1NTU8ODDz5ILBZj4sSJnHjiiemvw8PDo1d4IuULYPLAYtbWtHDY8DKwYtTVr6N61xNgQMXkvLTbWjE3usBMkc+kK42bFlJqVdMuclIKFHDTumfq+lpibRmjbR2tO9LfJ6PT3yS1b0NCpBww9jHK8wYlTBkIIV0rhpQ8t+1xWnadwvvXXYStbKSQqavb/hSW3zyLqBFk8jUvpz3/ffc8g17W+ZVXSrFpyRrKB/elsKwk6TbSyGMRB9Gux5Jft5HxpZmkRgLdUW03W6Ilo9HbNjLvgWvd5G3x4SYRz2IrtAKtEPF5oRXDGz4E4MW1S7l1bwVKQ71tEygIYPilmyU/Hivlzru+L0X2JoqMGAKBAi6lgkHb6tl0x+Oub5ISCC065ptbAyDBtpNHCElpuG33u2bbdn2slFmT5k7Z8WGyNFjpRQq2O1SV7p4r2x3ESzgBJyMaH+4JpbGkOLaNI93v66ZNm4jFYlx44YUMH57td8PDwyNbPJHyObChLcJVa7ejNRSaBj88ZABDdCsnDM3j+T/+htpdixh9jttWpntcBRzL9bMwfelFilYORY+46ef3DT2TgenaOiqjQ2aOEWCfakjbRmndIUSSkdAv6dxNEkUDzdxi/HnFyc8XgYg7mpgy81dUoNGZnG6ADZuDaNVKzaYdLHr1fTYtfBc7uhcQBPKqkNKH1orBBxzKmMOmMWDsIMKEuF38DBRsWPcJjx+aXUek0SmdM1Ox08qnBBi+/WlXiMTz4cdlCQoZXxpfJiQCt3NeXDCanZaFwB2ykG0avzLjhqSug1QuhXYjjSpIwAwggU8GtNCvLkwwMQ6VcHBKuNjk7IR9EMtJU6VZKyyre80p08xF2RIz2jMnUMe90jZCZYjQshQiyTBQAqXc40qZWsjYtsIHSDP155IQKblpHHBtx0FJg9raWt59911mzJjhCRQPjy8IT6R8Djy+u44lze0Ywu2gf/ivv3H466/SBjgD+uA/oLNj1076W56wpPgCqX+QAcIPnkUOYGEy4ML0wxBKqYxWEqU0+UZO2jaOJq0lJSFA7DSWlLycQQAImTrHi9KdgicrtMoq6Vpz7aNEwy089nMAQUGfcYw/+kLqdtRQs2k1oIm0tbD2w8dZ++HjgEk0pwi+8UMA2h2Hbc27sLWDoxRWPHomYkdZ27KXETl55Pn8BAw/zUYOhVnmb0lQFxxMWNYw6ob3e7UdwNwu84N/9RqHTenLoyck97uoi7azbO4ZWP1+ywkj4+r5sPT79+1YBvc/D2kuSUIPkQJgt+ViGNGeG8TROBktKSKmkWmsi9pJWFJSi5REYjkjjSUllrCkpApPw7WkKClZtmwZoVCIo446Ku25e3h4fHo8kfI54I8/MScsCIWvv9qxzqkaTOuufRTob9NSG2Xvku1wbup91eyqBqCpsYlIOAxao5TjhnqiUY4brRBVPnKAraOvYFi4DfxBRIqMslo5GfNvaK0zZm61M1hSEgImnSUlcYx0wcC9DcUVWpFNmPWIkX34ZHkLh5zzY0YcOIY+gyqTHnv7ynXUVe9l1/pNVDd05uaYb49g+uLaFHsvir8qIAIF93N56wsckPVVZJUaJssd6Q7/oOSr41E8WeaX6crSBavZub61o96Sm10nHooM1Nfs5o17/oqOO3wjwC6OIfy7WPzhTUhMhOyMGBPSIGzvQbbazH/rXQzTxDRNDJ+JYfo63ssmBWmEe8KSItJY3uqbo/QFdu1pY7yjMOJ+J03NEbZWtzBhVClRJxHdk1o0OY6D0PDJJ58wevTo9JmcPTw8PhPeX9fnwHVD+3LFwHKabQdHayrfepO3r72WJg3WkUfzo29+E4Bnb74Bbad+0lv00rMsf+ohBPDmu+/wxodzUrYtooofAiPW3gFr73D9DRRoJVwnUy3R8RBixzcZIcemvQaVhUhZZ9us29OIvH1uPEJFxCfXafbVumYAfv3Oh+T74uuh25DDNh2FnIK0ScGU7l32+gmxZRDL3G6hWY0fzdtz7uTt2W7Eh3vjEvO4PiBxBw6hISZygSO5RG9nyuAiDCEwhIxPnfMFPh/1TgChLSKOxfpr3FDjO57rdOIUibwuQMDI5aShVwGgrTDCF2Jy4cm0TBiW/YWnIpMlKpGCvhcipaCgDGHH2LS9kU07mpM3Mkwi+2pYuXFVt8WDZvnJGbaHRuv+5NsNgtA8SeEPruh5qkDCNrM7ECDVoFHCkiLTiJS2OtdRe9/r1dzzavxhIE9S2OoKk5dKTKIzitGi88EjGY7jELCitNkxDj300JTtPDw8PjueSPmcKDANChIm4gEDWD1lCm1tbRwxsNNbxOcPEMhJPaRSv2snAug/7TDy+w90q+0K6fqWCrcCsJCCpq1b2bR6E4/6DuXUg8bjE4AdjU8xtBVGx8IQi4AVIdoeQgfS9/paq9TOqfuxrK4VhduvO/FUc0qDrRW+fB/zjQAIGXf37KxH46YgCVFavwezPRfye1oyIDHc0zu7QlSESD9ABnv7RLH6tlOZU+AmDZMSYbjOuolJGkY8DFkipIFlukMMxWX5nD30iKzP58+AlgaTx03uqKjcUfUYjXYkxMAy2sjpZ4OyiNbkES0d2qvrToruHHpLulonkgT2QqTk9yNvwydMOmUyB59yZbwqs3YvS8bDkaWBYcRDnw3X8TZRZVopu2PSykYpy33vWHznjffJK4FfvXQUtmXjWBa2bWPbdkcl47aN36Zhamqh3WFJSRN9Nros103/33VhQLLHJ5BRRZ96G/HKHkSRSHv/rFgMA5gwYQKlpekLN3p4eHw2PJHyBXH+BQNpb9/MiOFHdyxbv8D1HLjhO5cTVyAobVEYaKUw0E57QwTwceYVV+IPphYzNR/OZvuCD8A2KLzk1oznYv3ud+i2trRtlNbIDEMmW28+Oe36LS/NwzfXpnDKKPKrkudlWfz2P3j/oRepG3gu0aZt8ayxnYJEa82aPSGqWwtZsXRBxzCX2yGq+Gu8ffy9kpMo9FlElryA0tpNl6/i+V2UQmv3/Qgf3HfAXpZ+872019HtfOt3c+/yPYTN7OokJWgtLME/aRpHXPH9pOtjre3U3rgYZ0yIiguPBOCNOxdSVPN51IbRabPwJopYIrL/80/UYwrkh8grTh4JlZ7U/iQ7czfTL08zdERyK5KjFO8rCxlI/TehVWZLihNzr+Gk709idUMY0yeYMa1/x/pbfvEhRXUWZzWnT60vdm4HYMaMGWnbeXh4fHY8kfIFsX7tbxC2j4KXDiQwsIW8I4+gqWQo+Q1bkYV93I5Wa+pbWog4mrJQmFxfGN+QZpSKAd1/kNu3bGHRb35FrG8FGzatAwGtWVoblM5cW1eTebgnE4nwT5Um/fyWpcsA2F77O3wtydtVt/4VgFOfSh1J0pU/+wqYpj6EF9Nnat2Sn4cqTR5RlIraqEWgdTYPNrezb+9Svv6zf9E8dTrhYSPjCclERx4WEB3jLMFwGNGWuqCfiFvd9H6f4Wf7BOLoznBwrTWr61aT68tlcOHg+PpEZtnso49UQgRkaW3rDTEEwTS7jVitlG04k4pVNWye/xhIN+Ge++re81hrAyXqZJoX78MqXeF+FyUgJdInwDRwtjYhgX0L9zB0YB5SG+z+eDfSbyD9Bt++eAKP/PMT+je2sXNfHX7TwGca+E0fAdN0rURCIMLtqU/Ww8Pjc8UTKZ8Tm5s2M2/XPI6sOpKq/CqGv/l3lBkh2thE/b2/oux732P1xPMYVJrLn8/pjLq44G//YGfMxx9/eDGL3ruRJv0gc964lGO+8u9uouGJq35AYwjY3IghcoE2tOjLs5c/jCE0UmqMeBZVwwDDJ13nQ79BXQPoQHrTvurFcE8qEk/vOo3nbG7FdGA72xYfwbFnnhbv4EViBwgh+L39B6rbKznpsms7cqcksu66xxCdw1+G5Kg1v+APRiMvHzQGQ8oOx0y3U3GHIaSUzFv6FM6GO3t1Tfl6HwX196ExmbNX892GKHlvvw1vv017wLWuuHlL4hvErTzbh1WQtyV19lhpSrdAZBffnM/DcdYVv3EnZq2Z+PDEjnXLv7E8/hknaif0QqTEfT6sSM/onc9KTEqCRuqrtyIOpVtOc9sW1bg1CZR0Jy1BCfwqj4AahLADROsak+4ncbX5a+pgTR0a14G7q1Q+C0FDKMpTV1yU8nwKgEh5FUVFRb24Sg8Pj0+DJ1J6gXY0qjWG8BsIv8GyFRdTX/8hhpHDw/skC1sVN398M8MqTuDUGRdz/Nx9hFc/DIBZXo5dpzH3M8NLoXG0u+yAw37M26+8iq9wKW89eyrHndWZnKw5fxDY2/Dnn0tVQLrDHFpga0FUSZQjUFqiiE+JeWHQWFiBY9anv7YMESFZkdg+TVr8nVH3K9e6qoadJwzliGnje7RZGbkNf3gX4ycdmNVhrZ3L2EmIyvLBadsZpt/NqZKFk3ACv3A7559MvYpvjv8mK4Y9Q/gnv6Y13+TwtxfgM5MPY3z81VMoTRPGKhJioXvtyM9MQvNIAdjdM/ruadtD37y+WDE3Qkm0rwGSZ2fdn7qd2wDYtab6czjL7uzLK0bbKZxxAUe7YnDLaQM5/NDDe71/rTTaUtjhGO31MaQpUDGFijk4MQcVUzgxB205tL67lpCTx4DvXI3t2CjHcf1jHAfHtoksmY+9aS1jjzmOQIY0AR4eHp8dT6RkibYU1b/qzEbh659H/Tg326fjtLOwtXN4Zm39Gub3jXHJHw4ntmUA2rIIDB+G/bePMPfP0aAiRG33x870hZh53Kt8+OEhyOI1PPjCTWzMmUFbNMr4049nw3svkD9acuZFZ9K/KHWdlf35518eZ29j6qEHSIQgf0ZTflykKDt1npScnBCJ7mjhn3/G+0aIXz/+7+7nEioGJ71fQFdmhteTLyVkCPhNWIp0vP5uNiT8X4x4JMyEo85i+Y1h8n/0e9792y84/so0PkHpVEeHnuuuUnorE22lCStFxFGEYw7NsYRvhoCP7uLYtnZW+/005xZz3DPH8cDxD9A/0bnK7DvZwvI+9DtkDzkFUZbN/UunL1HcZwgd9x3qyJGjEQKsqEPLnlKEXdnhAI4Qnc7gQqL7V/GqvwillFvNez+s+Pepl77UHQgpEAEDfyCEP8PfzYrFSwlETc455uik69dXVfLS7Tdz3EwvN4qHx38DT6Rkyf4l4K3qVgoPPZBQsD+5uSM4yX6fakuyzZzCFj2Eb/YvQwhBYOiQjm1sJ4klRdewvm44Ly36kFMPPJxgThGHHzGfS+bdy7sFx7mNTCAXOOcHANy1dB1HhA2ePmlCVueu45lL06G0ThvRALDuX+8Q2+3WJHJ7GXe/Ip49nXZFMWWdjplJyMlxO4mW3Ary2/aQ64S5655r3c4t7hRr1koKxECa7n0kXiBOdFQsjofKxC/MXeD060MsJ3P3Ho64hv3DHzo/Hm3soHBQ8WKKCqejmKLWDlo4aKJgQHOks0Jwy47NFAtB4aOzaf7Kdgq6RHAlELZizd5qNn7trPhQlYxXl3aH1rTWnDHoSnLWGOz8mSt2JwDJqxklpz5sMeXmtyHa835Pal0My2/ka+qP9I2NZVNwB2ERpWHRDgbNPAgAFcg+ksgfyqN8Yj1QT100yyEzDfgg7Auy4ZUhqdtdfiMAa2taGduvoMdqoy6CDeTM3gUzBmV9zp8KR6NE6u9vS90+TJ+fUEHP8/Tw8Pj88URKL6i6+XAaX9pE69xdFJ0+jKqpT3Wsu2XwdzNubyuNsd+T4k9OmMgH/4T1ezrL2wdDhaxiAuXU8+ao0Ty2fjMTK8sxDZMXN7TzRMhmdsjh3jlbuOywND/+cVwrSYY2WTjOBtdIckUpLboB6OIsSuIp16RZNlBWeUDKfajEeMTxU+DZ1wBonrvSrR0Y312RVUBZ/iDat+e5lWXcxCXx+a57iwulylLeyAnwizfeRQsRn+Ip5AUo6c4vqc7Dah6HzPdjCNMNmRUGhjAxhIEUBqZ05w3pLm+zWtgce5Nc0a/jqL5hQ9k2eBAfT5/O6w88wGS/n1OuuQbD12n9mbA3RlNZP/KOnolW8YR88UrPUrphuvtie6gsHorpcy0aNVsbsWLp0tx159FV1R0C5ahDqhhanEPAlOQHfFzy0VkA5MeGsknWMDjSHwMJc8AemCohXWp8/hxAkBMczfix93ZYQVyLiBG3jsgOK0kiCf/ct36OP3clP3joP+6wi9LxocpEdWLos24v10ZSD/eUDS+mGqgr+y8Mr9ikFSlaKRzbTrnew8Pj88UTKb2k6NRhFJ366RJuOUrj22+4Z8Lwr9E372HeXxfhRyc6bqE2wBRwWEE7lf368eN+nR3kUSPg9zGb899czW+DjQxaWcMJ45PnG0mQnSVFZRQpSju0Dmpn3PdOS9su/bm4TB89me8OGQzAmt/OIuTv7IDeuudJ8vb56PvLM7La55B/fcA8x8cj5CLjFhmhNVK781LbCK1R4ULs6q/z7u+Ox+fL7qv/4ZbVfG/2m5iy0/ek76QjeGrGIgpsGBCOsBRYdf31DBCC8n79aKipITq0H+Pzq5h65SVZHQdg4R0fU1qbvUg5b0xfbsNNnNYac/jVESM6Vw55Gn3HATwZdIcoh7ZvY0ykD1IYlLVWAMTLDmaPEBLTHyS/qH/mxkAsYhOL2hBw8AdTh3BLXyNEINef3IdHGJKd+QbRYO8z5PYaR6PT/BlsXDSfskGDP/vQqIeHR1Z4IqUXWJEIT/zqJxh+PwefeS7Dpk7HsRQ71tRj+CS5hQFKCsPw5q9g+ePuRsdcD4dfDcDoRe+Q98YWnn841x17l+4T/3n1zTTl13LFC8+TG/IhgMa24bzcIDDsvdw2/WIA5qzczas76rGEpp8pcQzFdVuq6f/SdqQUhJVml63RBX58fgOfX2IGDNqbYxm9MrXOJrxU9C4VbBJU3Adjct/RwDwAfvf2q9x0UhdBInAr8GbJRZubuHDDXib+/uyk691cKZpL73qdd4HnN+3FVpqIo7AcTcxRRG2Fpdx5W2nOGVvJ+LJ8osodgAnbnWGnD7z8AHl2ARNnTeTMw85k6+zZLJ87ly0NjWzau9cNrxoyhDyzmKm9uTmatB3k/pTnBjjxyEG89sG2nitLhrA+cgQ5bSbtZTZNPsnc3WsRZiXnjZsJi+MH7BXCrZOUJfNfewtfydtYbX3StgvHfU5yAql/jnxKg/FfEAaqZ1h4Vxp272LirBM/c7i+h4dHdngipRc8ct0Padi1E4Cty5cybOp05j67kRXv7exo862jnye4+vHOjRo7O5AzN7xP0LFoc/qA0vE06ZpJ0UYqNjVwwxFBtpW4VWv8KoZwWnhz7bvc3nooUsOT4VZqA5JKBwIaJrTC6HqFv81Ca82+VpsdUQW72rr1P61l+3DM9EnCshnuSaS3/ywk0uFLIfD527BiubRH93tCliB701vr9Hng3RBm2O1IQHHNv5Zk3OXDb2wgmOsD/wp8feCjrY8yQeykpS0CW8H0w4TGaja+/xQIwbgZwxkHbFy5lbLKEmav2IURUFhrluIbMzn7S8m6pctlBw7i1Q+2EbN6WmB2ln6D3KZScmvc9zLXfXWsrj49vSX7bYoqHfaGQVsVadtF4vVyUllSAJpshx11bXw4ZxtSCgwp3FdDxssySAzDzRRrGG6oesBvMGhI7/LioEibPqa4b3/2bd/au316eHh8ajyRkiXL3nilQ6AAlA9xnQ7LqvIAyOkT5HHRxhWrH3IbVE6ACV+FUZ0hnkOKgxScdCblV/+o274XvHgfXHsbfz7mfqpGTelY/suX7+T5uge5W7XjCNAhyTVmHlfMHEEydt26mLztLVx050zA9UWxIjY33TIbgFgs5hZrSxJBkW1YbmtDXcY26UhEswgp+P6xpdz+SoTnFxhI80luO/U8t5EQvbKkoMkqL9nMsT6WDSvgrrEDyTUNAoYkYEr8UhIyDfyGJGgIGiMWv3x3Pc1Rm9xYCduARW0rWbRyJSMbRzKBCaiY5LFFdUCS+1HrZvctaH8T88nfoa7ZhszL7GgpdGcJgWz57n+WIYBLp/d0KD3m2q8yaWcrT934cceyb/3pcFqFe869l5uiV0NEw8fPYM88P9Kf3gcm7LjDckFf6g/xT1Y7q6tboLp3379/nTyOmYcPzrq9UG7qldRoHCuLQlEeHh6fC55IyZKxRxzFOw/8HSeYS7jfEN5dvor5G7cxePBgrrjnWFbtauI3f53DNcZ3+EnZR1jH/hmr7wEAyLBbpt52HHcoYD9UvOCb3G/dEFsQULDy2PRP4nbEJtIUw7Gdbh2PEAJ/yEeJOYh6exs33XSTu0ILN9IEicRAIFFEKK3NZe3P53Q4sBJ3PE2QL3zUbN7AY9+9sruTZNxp0jaaCJsbMIwcDCPUxYmyM/S0pcH9gfcF/Fx1+DE4+hX++io8OzefktCr/HLWSWipe2VJEVqkNdEn2CRMdIGfs0ak9+EZUBDihfPcHC07dhYQenAnq4/4DUOHTcOQJk5Ekr/+NZj/Nzj5z+i+U+JWCRW3FGlwFMGPQ4i189BZO1r23rJRkOtnN/D4smpOHdazFEFZVR7jDu/Hqg938Z07jsQXMGhr7wzF7i3KsYnFOoe+OoWtiM+7dXyEEITyCvE7pxLW89LuM+IoTEVS8Zxgfa5kfFkBfzl6FLaj3JqQSuE4GqU0Ttwp14lPdfVhrpy9gXA0ex8f9wJJ+6toRaKUDfiCI4w8PDw68ERKlvgCQY677EpefO01dCBIQ2MTDY1NNDY2cuyxx7Jqlxud8G9nJo+OPBe93YTta7rt4+n2CLvDMfbvShK5JYz96o5EtzYhCjOf23O/mkdti5sJtCiJc2GRNQy7MZcR0/pgOzaO48RfbZRy5/2N7QwRfXFyg4h44Tji2UuFdruzxr01YJqEggWdNXQ6ojQUtnYQfrezduOSNe6lufvRWiPMKGXjmvjF42/hN3LQBABXxP3zbc3j7z3J0/mPME7PJXLvxWy5/XX8Q0bhGzjWrceTKPecyM2hNaphC62T2njruUfc5bjr3VBoVzRoNGz7Eb9ap7hxwTzXtSJe/Vi4lRJBaypbdzPeeI/+FbMxlE2h3UaBoygum0BV1cGdN3Xjs0ArFJVCVfLavBsXfshw4OCb3yeM6ziq4tIgMXVmFXGnacLkkMwfOQAxR7Fu9T7MHJO/npC6+N7h54xkyvGD8HVkHU4Ii96JlJaWPLRey4dzsgt9ByAAViy9T0rMURgZDDSOowkV+jls5w4ADi7M5fkpyS2KANs3N8DsDRhpcvYkQ2iR1pKSV1JC897eR0d5eHh8OjyRkiVCSiYcfRxLduxmy5YthEIhbNvma3UHs/NnH3J431wuO2kkf21tQodMnj1geKJrTPT1GMqhLYmRXcWdEfd/kjQrcyCc2aIQiTpUhAxGz+hL6fCeY/BC+cijglMvOPJTXXuCbT+NMvqAsxh5UfIOsW7jfJZtv4DJg56iZFjybLF/fOqPjOxzL7e9F8G2NQJNnoB2baKQtDv5jIzMRwiNWfM6ALEt67Drm92c/8K1ArmxrwACY99WArkOzZPzSYS+JqbOpG2CKS2bcRjjaihDgD9RBZmOyr3RT8rYZk5FDQ6ipQ/Ln4eVW8FhIw7tfiENcV8jK3UdlwafKwLOGVqOaYaQiQRm8dwysiOpmbv8sfV7WJWm7tH+XPbKSgDsmEOf3NThuYZPUlDWJYlZx1eqdyJl3boZ5OY2cOCBB3bxZ+lS8BG3eKFOvCrFlg072LsvdXFBcEVKph8i7Wj8XTL47rPSW6es+DX60zjjJkNogUiTor9i6HCWvPYiSnVG4nl4eHxxeCKll1x0UfeaHolEXM7uNn5y/mj+vqqdQSE/hxbn9dh2vlLIJKnSU1lSZK6JyCBSHrh6NuGYonJQHuPPTv5ErxzdmzItKRGQ3kE1Hn2hVeof+b6x3QAs/NVx+P3dBVVbuI1bb7mV+UxhAmsovn4NPDGGUH+Dll//AInGtm1KBgxjz6aVtGxYQ+ucOQzfB5uDBZxzxqK05z8v+CJLXoSfXXsgppk8o+0jv3iI2qZSytuKUUq5+U3UTj7Y8Lt4bg+F0pqStjAzACdUQqquSsY7u+9/YyrBYOYMwU/f9j6xhuyrIO9uire1NbVtUcrTCJWuiE9pSZEiQHHRUKZPuzLrbbaufI621rVp21hKIzMN1zmKoN/g18MqeX1fE9+pSm+dseLOuP40fi7JaBzxH8IDFrL7jWu65Q10EZCvGX6GZNfmD6kaPrNX+/bw8Og9nkj5jBSePJSmVzYjgiZGQYCv9yvl/up9XLVmO78a1o8yv3uLtdYYSvXwO4EuPin7KYnM2U3cp2iAUUdUpWyj1edQlwf3yT+tg2oWfV/VgAEolTySOTeUy0Xfu4iNd66gOnYgNU88gx5cQfvWWszLfg6AH2jFTcAbFOAMyuPpYwXbprVxTqYLyOL89oQlPgyWbG9Fot3IkXiKdym0W1hXaBzHtQ4020FSxo/ELWQi2yfuXmiGmFJsCnc6cNZH7F6LFN3L6J6OzMK92ihzE0trzEwh8sp1rP3ewHK+N7Cn702PfVruvTfN3okUVVKD0V5BMJhwYO9Icxx/5xDNfY2YXAPM7NW+PTw8eo8nUj4lrR/vpvHZjQRGFlN18+FuJs2ow6zifOY2tvJUTT2nlxdxdKkb1WFrkEohzJ633HZcfxK5nyUlG8fGof3zqKlpY+CMfinbKKU7KhR/WhL1eNLuJ7HOSe0HEAyEaA+TMnX+kPIhrAydxgd7B8AHwOBfY/ZvZ8a8n2Moi40zBhGaNInhR51GQf8hjCvuy91PjaMwCx2QjQWhoqKC5t02V13/x7T72v3RU/DmK2Ck+RNK1LHJ0owVj3vKqm2LrbA2d2ZpLc9JP6TSnU9nSUH0XthkE+ZsAWlGWIg5CqEg6Mt+eMWxE87ovRMpwoTi4CQmHXt7yjZLl32Tpqb5QOYs0x4eHp8NT6R8Sppf3wpAdH0Dd727gfDCPXytQTMCuOQHY7h2/U78XTr0mFYYysFIYkmZv3s+ZwJRp3vllqwK4YnM/YBWGrl/YcNeojo63DTDPfF16cVV5g6yunYAAGd9q5hl89exaVU575xyCt/6xY8ZU1jao700C8kJ5PRYnop05ydNgVaZO7aOa1WpfUiEVjhaZJ2ddNPetqzaAZT6TW779kFcfd9CAJzeiAfx6USKgE+ZWyU9dgZLSmu8aGKgF1aRRC6Y3lpStLCQIr3gKys7hg0bbiQW24ffX9ar/Xt4ePQOT6R8SrTd+av60sub+Icvp+PHf/Met+KwaLBY1NSAFNCmFGWWReE7b9E6aZy7oXDDNo+MDQXWsOqTdxgYbY6HB0N12w4cHHZt75JRdL8fc6s9iq00C1bvYmhFMXl5JkG/2S3niVYamWFsftUfF2E0hNFCoISbr0N0+J8KpNbkE3c4TUWiM07jk0IWQw1mAJwIVB40mWOmjGfFj14mNOQA8pIIlMThetkVpT6230hrCerYQ+Ja01zHlr3NjEfipL0fnQRMSTRusVJKoRyFcpx4aK0CpVDKzYjrGAZWWxgTVyR9uKuOcWV5mFJgCneSAkwpMYXAEKJjnaM0WouOCK1sE/QJ0aX2UpYk/DrseHHHbqHi8fFMSymkcH1TjLhDcVcSGWl7I1KaW91CmA89+ygvPx+Oi/24o3Lnu27/C2DsgU3ocHq/oLzcUWhtE43u8USKh8cXzGcSKTfffDPXXXcdV111FX/5y18AiEQi/PjHP+bJJ58kGo1y/PHH87e//Y2KivRZJ/9fo/9v3WgPpTSnfe89FpkO0/NMaizFug93wcQcztq2vds27wGhVSvZ8a1Luy1PdL3/WHQ3K+q6/xCXOyWov3XfT1fMsEO7rVn017Uk3EYdNLYAR4AjBfk2RNtt7rxnCT+4fErS/RTWuz/MzUV+/I1RfFoTEwI71wdolIamgEn/KWn8AeKWo8Y9SygblzyQtnlfBDMX/v2vl9G60A0jhs5wYq1pFxqDPjx5zRtssrdRFBvOqrZH+NVdT3a074icQnNn9R/ZHahhc92jnb0iAMrN86I1CE24xgByue2mZzozvCvhpkLXwh1SaM3HUH6u+cM1OMoh38nHwOgQVYnXvno3lwnIf/R4rI77LlGuBwsKwVkiDAKG//pV1zE0keh1v3viXofAQXDzvHtZM/onKW+xEoKXTjuVSMh1xL0wXhLnk0eX8EnqTyYJFwKrWcxvup0FJLR2TzGidQjH6Z1IiTbVYwpFzQ0fpWxTNDrArkF+Bnyw3D2+1hiajom435UZzj7yqd3vth0/YATD80PucGw8EilR5DDpJB32T4C8Py0tKxDCIDd3eNbn4+Hh8en41CJl4cKF3HvvvUycOLHb8h/96Ee88sor/Pvf/6awsJDvf//7nHnmmcydO/czn+z/mqZXXqHhkUeReXmUXf9L6golATPA8d8ez9z/bOCFhijDppRz42F92SjseBoO3c2w4PQdQt+Lz6MzNtlduUs3cPmAQrQUaK1QSrP39jtoOmw0e87sIlz2e+gttAUL571FrbOLi4f/hGjEJhp1sKIOdszBjinsnWHMsEPdhqak1xXd1rk8VB/BF3+SNZSi+Fvjyeubm9X9yS0eDEBrdE3KNnaLiZkLO/duxLLyoNuAlvs8a1JAjiykrsWPDuaxvO977AwsZ7uio73QdNu2b7SSWM0ed0lHIrjulZrLYpJtRavIjeYQCATB0Aifjocgu07BdlETUaedGn8NSigGhQaR589DStltmre1glvDghljB6GldEscaOXKE61wHJumTUtZpQby1TE5HWeh0a6lQIiOMgNCuGneY7Zi2Bu72DtqHJHDjwcpO1L6C8Od182NRKJ19M8rxRwzCRE0Kcn3d/muaRw0Ttf33ebdV19kHQN9MQzhnhW6U991fF3389z+eMFeHKd3lYgLcgI0tBtEJpenHLi8QGomOxIj5NqFHK2x4+ftaE1jLMoDwDjVC5+U+N/VpCOncuCoAVlv9/obv8Lwpy6GCFBUNA2tFTurH2fggIuz3reHh0fv+VQipbW1lQsuuID77ruPG2+8sWN5U1MT999/P48//jhHH300AA8++CBjxoxh/vz5HHzwwal2+aVl2yfLCOTmEsjJYdtPf0og7pD383+exPKhrni4f/cevnHwuTy+5AI2LakFrTnhsp4Jr5aFSmDsdEq/8Y0e6/YfyHCiUdZf+Wd2faWCqdNmpD3HP+14k+rW9Xz1jFEp29x6/RxEq5V0nX9gAXb/PFpiCiNkEjygD/ve30FxcwyrOQpZihR/qJhQeERCQSRlyOhB7G6Eb3/7UkorhqRst+Q7v2SePJriSCXfOu0g+k7+Xcq2O37yFv5+bVRdnbzAYFdee2gC11X+iPOPvyBtu5tfruOxusc4uN/BfOvYb/VYX/2fOdy9aCA/OucEzCRh5bvrmrn3ztson3QY15x9TMbzSrD8Vhtz0jiO+Mm3k67fuWIFHz7zDK2xKOMsgakFpq0xTQPTZ+IzTUy/SW5+kI1rtjFkxABGjBucZE+HZ31OCZYt/WOvazc52qHAzGX4uam/m8OA6Wn2sX1HEw98uI3c3jjOJsL6e+mLJYSDkOl9UgoKJtC///ls3vwXKitO9YZ8PDy+QD6VSLniiis4+eSTmTVrVjeRsnjxYizLYtasWR3LRo8ezcCBA5k3b15SkRKNRolGox3vm5ube7T5X/Kf3/+y8824wRyxdjt5UYuWkCAU1Zw2XxHclUfdspcYPPU4lu3JxYql8GkQMuvoCBVz74nO4ofZwATSJ7fSOrXPqxCCwT/onnq/rbYd5u+m7cFVqK+PoXBoEdUf7KTi0L4EC9I9Taev72LEw3FVBr+PUdddzrxb1gPw7L07uPyvgzH8yXObJIaKsiWbqKnigmKogyd3PsbhyzSRaITBg0+hssJNZGfGOz/LcZKKlMZWN8lb//KS7M9LawzbhlDqJ/nWfe5+m2KtfLTsnYz7/GgpXHLRZQwc2jfr80h3fulS1yfDdhx8nzHpmZMIJ+6F4LDjw1K+JI7q6ZDSQWYQKQDDhv6IPXteZtPm2xgz+qZeHcPDwyN7ei1SnnzySZYsWcLChQt7rKupqcHv91NUVNRteUVFBTU1NUn394c//IHf/OY3Sdd9GTjjZ9fz3M2d51fTr5yR9a08Nusudnz9G/HhmgB7Abn5HpjyY3atS14ETbueh1kd17FckZIsZHl/pDDQIv1+Xct99j/yw78ynNWL91BgKcKPrCEMGMCmdfWMuyq5XwvgZoNNEV4MdCSzc+zkVp0EuUOqOP3sGl74jytaUwoUoPdRKpnvw6UzLuWuLXfRLNuoq78DgFWr7mXJkjFIWcLe2rHABGKWQyiJZmtqc8VEbk76oYOu2LEYhlKoYOpt8mWQsprDOekrhVTMGIcVs7BiNrGYjW1ZWDGH5YtWsXnbRo6eeTQvvf0f5n+whIFDT876PNLRW0uK5diY8rP551txx1lfLxxnEw7IvQlbdp2IsxMpPl8xQ4b8gA0bbqKq/4Xk56cuS+Dh4fHp6dWvx44dO7jqqqt46623CKb5Ie0N1113HVdffXXH++bmZgYMyH4M+Ytm6OSD+NZf/8l7/7qXWCTMCT+7AV8gyO4bbgCt6fenP5Fz4FT23XsvBaMPZNHbDURj+5jzVKxLAT7XryDgWJChc06gYvFEXWk75+zRSiGVRTQcwx/wZZU3pfy80ez9uIbQuvqOL4qzL8zuBTX4gpKS8WU98lAIJJrUDo6GdK/HcTIX3csdWAk0EYrtxd6zA6woOhZBW1GIhd33VhSfUY3RHiU2/y2wY2BbaMcCxwbHcifbRjlRzmhpZXfB+ozH7hoq7vNdhuO00d4+B7A4Yd4LnMALnOoby/a//B6BRmqFKTWWkUvYX8LqcAVQTu2il1m0ZSHBorH4QyFMvzscYwZ8mH4TI+DDiA/RWPGIFCOUOpzaao8hEAQLQuTmh4CemWyHjx3YMf/6Oy+ze8+ujNebLb0VKbZjE5Sf7bfCjlvdjCQWq1RE48OygV6IFKVsN2mfzM7vpn+/r1Fd/RgrVl7B9GmvYBjZh8F7eHhkR69EyuLFi6mtrWXKlM4nacdxmD17NnfddRdvvPEGsViMxsbGbtaUPXv2UFmZvPJsIBAgEOidM95/m6KKSs746fUd76M7dtDw5FNQNICWOQWY/SV9r7+euXf9hVjz2wAseLbnfo7QbUQaU0fqdCUSccOYVzWtI5NHg85cXx5R9z7B+o+565t3ABKEiRAmQsYnYTJv4jQ2DzsAKfwdzqmiCMSBIYJK02g0MLpmGwe98pEbKfEMaIOOqAmFZsTEBvJI/XkudAJcJZ4htDFKnzXL3Egk3Pp+ieJ7iaCbklg9CyrPcjf8e+prq8yJ7+D1DDcK+C3wUDCzE3dbuNPyN+PQH2IYXZ6uPy4EwJcfwvKXgjDQQmI5YFot5ERqqLQdtlPO+pp21tdsAbZkPGYgEuErgNCphakVr6jtz8ucZn/3jn1YhGmIVHP3rQ9ywimzGDbGfQBY+OEKNm/YipQSw5BIw+iclxJDGggpMAyJYZoccvRktNa0tLQwf/58hHCrHcfiYtqtSOyg4kUgE68NbU2gbZa/uQDT58P0+ahTPoL5hfh8Ep/PwPQb+H0Sn99wJ1NimhKfaeAzBA1t7jEiWYSGJ0hYUkL+7H/ibNvNUyRoyaq9YQSZNPE+Fnx8Chs33cqokddn3sjDw6NX9EqkHHPMMaxYsaLbsosvvpjRo0fz05/+lAEDBuDz+XjnnXc46yy3c1m3bh3bt2/nkEOyre365cZxHP5w//2YJx3HN3xnANC+sIbcyeV8PMf1EZh51KkMOe2seOBOZ3jj9tNPICcvyye7eAE1MxBCadUjZX43tCJTppCovw0TmHTcRVjRWMdkx6LYsRhK2awdMADl14yVPtzSf11DfWGzKGBzn34c1uAjRwsKioMYPgMpBFK6Scui8nWiMrW1KCc/CPugRCuGRk1MIfHH83cY0s3lYRjufK7u3E911VmUFAxzk6gYPjADCH8AYQbQBDHK+oEviPAHwedDmD4wfQifH0wTYfrB5yd2YwkTKsZnvP1vfeI66uaiWTXvERzbrRqtbYdRZgBr0EQO+vrbKbcfC5wE2JtXw8OHUT/17+ixh2PHLJyYjR2z3MlyULaNbdtYW3cCL1Bekjp7sNXudtj+/MzWibz8EEX+StqiText28ZjTz7Mz3/1U5Sjefudt4jqFnd4ruunnMJQsmrlKrRyh0Refz0LNZhAQpgYz330WrfFj0WmYKWsetSdxCld/OIKeHFFoq5kl9KRAokigIUvXnsy6gjAx91/vQPDkD0sQIn3XV8No50pU6E9/ChLXns+vm/ZJUZMxuPPOjOrCCQqEGHnzoep1EMoHNXTKd7Dw+PT0yuRkp+fz/jx3X/gc3NzKS0t7Vj+rW99i6uvvpqSkhIKCgr4wQ9+wCGHHPL/ZGRPMhLDAMrn44XtdzNr+EX0Pd+NTfjBg09z10VnU71oLlMvv6zHtk9P+zMAiy99CeIioCO5Wcevbvy9kHD47SzYcjd/f3iSO6SAm4pEaAOUGf+JlIRlGKUKmfXLNzBwk2EZ0k3gZUg3qVe/1ghO0RBmfeurKa/tT8+8wYFmlAeOTy4oj37tQ0R+AZf/MrV/w2uv/x3HTu0jMjDHBBR/HB/kmH49I6D2Z+HOozlo17vEjvsRoYGZ22dCatdisz8qnigt5sSwHZtXNzYC0IbgzTuf7ebHMniIQevucI+IrGToSBgfDkV9K/APSy0+AFrnf8IOwFeQn7KNHbEAH778zBFX+UW5/PDnlwNw318eo7pxQzdH90F9xnDx98/tPFetcRyFshWOo3Bsh5amNh576ElqW92EgiMHTmDaEZPQWrN8+XJWrlzJKaecQnl5edwSY3RMUrriwKcNlOVgWTbPfzCf7esX8feTx1EYCGJZTnxSWLbCchS2rbAcje0oLKWIWIqP9rUgC10LndIaR8XDqZUb4r99xw7KRQt9+/VDaWgPR3CatlFSUoyUnQ7ruouT9f7zyjGo3TOEfsFG/AEZl27x9Wjc4O6u/wAUBZEgESMKr14DRn8Ynn00l4eHR3o+94yzt99+O1JKzjrrrG7J3P7/xNnLlrG7pZXBN/6O4dOnI+IRD2YoRJUZpLphb9rtxwyMdD4JxvNlJDK7xhehtGD59iKmDZ7AEWPG4GiF0g6797xKW7iYnNyjsJWNrWy2Ve+jvrkfU8vy3JwYKv4jrhS24/6QF2voI9I/uWohkGl8UE0glnp1HAFpnHjdSA+388mGvQVDYde7DHngMKyf1+DzZx7myHB2vLr1dS5/4ECUUKj4IJMSqocVoU9rIXce+wfKjynH9PkxTB+G6SN86zRM0bPKdVL+44Yvi5zUwiOBanUT6sk0Qzl797r3bfmjc/HnBSkf15+qQ1KH9yY475tf4d1XP2L37l0Yhkn//v2Zemh30SeEcCOVuvh+5BXm8JNf/YA9O+t46tFnWb99BesfXcENN9zA5s2bARg6dCglJdlFMck8975Nm1pFQU72w7yXZ1j/mz/fTb0T5qZLv07upxw+bty9hb/c28ZBR45g+FHpQ9R74Fiw4ySYe4cnUjw8Pkc+s0h5//33u70PBoPcfffd3H333Z91118Karc1I4Rg2551vPf+e1RUVHDun/5E/TsnsD18IXm1f6Zv5Vc62vsLi1B7Iyn2Vk9pPz+H/zq1NSNBpL2e5VcvY1zxAGZOcwuZVVcvYG37i8T8fTnxxN92tH3x70+z2d7GD3+YOp/K01e+nrbODIASMq0B3hBgZ9QW8eRgKXBFipW1SDnqzJvgpn8CsHzpSxw4PWOt47RIBMMYwYnlB2NK052E+2pIw30VBksWLqaiLY9xI3vmE4lIE6Gzy35qOjtxRAnmyEkZ2zotbu0eI42VJCZDgGLxGr97qxdVc8C7K5h66VEEK1LbdvKLcjn9/GOzOudkVFSV8v1rv8Vvf+t+72644QZ8cafd3viURWIxtIacwOfjEJ5gQ14+b4+cwVMfreEkp40HZqXPLZSMWGsDAP7Ap3D0bdjqToMP6/22Hh4eKfFq96ThpTuXs32VG068t3I2AJs3b+bp198g96t9KKSWULCqx3aCVDVR9kvhmQbbaY1v0dnhb9z0FADl5d2f1LRWcd+CNKRLlBJHSYlMIzBcG0gmZNp6Nr64NcdKE6bclZA/wAuH3MTp837Oga99m8W+EFOnnJrVtsnPTjOtzzQuOCV1cjiAO+ffRLtKXvBPCwlZiBR3KEFjj/kegSxCyVU8bNnITx0lcuJPZ6KVQofDRPc18MAf1rNsRwnLrl/OFfccnfEYnwUpJdf+5Kfcf+eT1EW3YSuLYLh/r0RKzLJwkJi9rE6cifp+ncPQrxq5XPzCG4Rycjh7SBW7GpuoKilm5tBB6ffx8m+BA3jx3fl8/5AzM/69dLBvIzx4IuSUwgk3f4ar8PDw2B9PpKQhIVD2T/61r08Zm5aP4DTLJrb8Q2p985E+P8I0KKxtYIdRwfwn3yS3S70itxyKRKnsfvh8viIAckKdWVkd2yYazWfq1O7+LkrpHkXZ9kdrhciQVMu1pKQWD6YQOBlPP9Nwj9s5WRmsOl05/fgrWFK/kinrHmfqixfSGPrATeBmuWHGyrbQ8XBj7djuZFs8t7aaIZU5lOUZHcsPAnSGYS9wfVRSOStrJMWtq6n9zXgkDkI7SBws7cOWIZpUkKbCoejiARwibFbtjOLM205ZfpCyXD8+SyH8BsInwWcgTQGGwKlvwpaggj73+CkSpwkpEbm5hHJzOfeKGE/dvTXre/lZyckL8YPr3FTwLz04l20ft2NmIcASxCwLJ8uq0L2hDUEopnh6fD+++ckmXitw//ae3REv+dDSANsa+Gku/GjaAUn3Mejs38D9z7HPzuX9B65n5rd+m7RdD+b+BYwAfPNVyM3GU8nDwyNbPJGShsvvnsmOVfXs3tLAG0s7l/tEPec++SYA+6dtqx/5NehzGEs+AKjvvlIUE2nNrnNO+Ll0tUrs3VtLXn5PAaC0ypigTGtNptQoWoi0wz2Z89qC1pJQaA1vvHk2R818GL+/u1XAF0/sZWc53JNgytf+zrNPjeTMNTdQ9NSRWW1zOfT4CGwt2aorGJlhW1fUJb9h0ZFfwdr8NloYKGGihA+ExMTCsNspaNtJgbOHUPMHWNrgD/tCLHzBjYq7nhDHknyoY962x/jdNQb6bXdYxnA0hhIYyi20J3ViXnR5rykYcjBHbDmXf//4UQypMQyFlopNRhSVm48wQJgCaYA0JYYUDAznMjjQF5/P537XDIEwhFsjqOu82fW9G3YsDIE0BLEdrfgFvLP8HWzHTjrp9npGYlIQyEeaftp3bERqh3VL5yB9QYxACMMXxPCH8AUCmIaJz+fDZ/rwmQaGFFnlZolqjYngoP6VfFxaTJNt8/GOXWxrC/NsQxvrTHdo6pY2uO/NBTwwfgh7Gxspz89j+gDXobkufwgjimNsaPBT19ye8Zgd1K6BgQd7AsXD4wvAEylpkFKQ338VgT6NfHX42fz+jZto8bWwY8AWJs/No6Sllb633kpgyGC0ZaEti4V3raNU7Wb6FQd36ov4zJv378Cfk53DpYh/NF3TvYfD7eTm9fzBVkpl/CFPPvy0336kROrUMsQQ4GQUQ64/jmku5cM5E4hGxjBt2u2UlY0AwB8XKVYv0tjXtkQ5aN5qnLLDeWH47fxpUF+E4YYVu69uyLE0TTB8SJ+JNHwcftdczhzWl6vOnYRp+tiyr5WT/raIG/ukj7KBxD1N/sRfdcEtqa9fKf78tdPIP+Y8LvvOhcSiMe6oj7C3IcK+lijqpS1s9QkmHjkIbSuwFNpxc4o0lUxGyx38NHg6SjnYysJ2HBzthj/b2sFRNo52sJWDox3eMddSXfgRZzdMpCw3gOOA48De1npq/bkUOhqpTXAkQglQAqkFUwKFQDuWJl63mYzWuG73B5hODi15Dk89937athsB2NPxfqDYw6gXMmfAVVpgxytL7zQHMOxXS3u0iUZjCAExpTDjEjs3GCCXAF8Z437nrgLCts1tS1dzZ6ui3hfgK+viCe72tHNbcyt3btvDllA+TDyH73zwAmf84A8Zz08rxdzFf+Pj9o2Ut6zj3HATvlBhxu08PDyyxxMpafhg9iQcx/VLmD7tVW759i2c9OxJgKCkxfUZyZ06BV8/t9ML722iNdjIxP51DJk0rMf+hNyZ9bETlpSuIqVf/344Ts99WHVhMnmLaDRkMLMrIeNVcZPjEyKjT8roUT9j85ankfItAALBNSz/5AQmH/AmJSXDOmqpZOs4C7B8ZyNRnwBMzp98En3GJU8MuD+NeiUyUExegRt5olQbGonfl9lpUymN2SXrrOM4tMViRGIxwjGLiGXRHI0hrSiGkBiBAHnBIIZyO/xQjmtB8gf89Ovrp1/fAgDmvLyVtjwfBTN7ZlU2ZBW+WpMLz72xx7pUnLD4Hi5ZeTdjLxvNuCGdvkpvzv4tr255hDlnvUlhXve6Pc1NDTT/YSU7jo1yyDGddba01qBA2274sbIUytaomELFQ5O1E19mK9a+/RF5+ySnXHhK3PLhw2/68Zt+fIb7/sKnj+WEnCF8e9YdKCtMNNxObVM7W83zcWJRHCuCtsIoK4qyIihloxwH5Tg4jjsf3vA+h8bmdbuGzVu289RNv8HfvIeo8CNmXYCvMrX4DJkmvzhoIt9tD/PM+s38q6aRTQHXQfnq2nYIdUZfjRxViDRTf0eUFeHVOTfy1q4PedeuhyL3sy3Y8gqnjT0/i0/Nw8MjWzyRkoahQ37Iho2/B8AwcujnL+W2qnZCRYeQe6ifto/msfHoYxizdg0Aqx7/AC1ymHDuoUn3d3KOATGblb9wM552zZTSIQ0SvrUaDssz+OvWUn76wUsIQBkz0eJorn9lNlKAgTs8M3pYG5fvep7orR+ANHDt+wZI0xUmwkRG6pFm/7TXq4TATPMwbWZhSRk27GiGDTsax3EAzfvvXwPiRRYtPpljjl6F2QvH2aaWKI8v2MGtTgv4BUunjqYyP0Dt5s1UP7mYMZccR7Ay9ZNrl6huoEt69SyK5L1dNZI1VcP585sf4xgGKoU/z2Efv80hS97vsfyBj2v4t1zIS+cf1G25qbTri5IES1n4evknGTBcp1XffvVxWmw3nDnP7Gm5i0Rca5fP371GjRACDBCGgQxk9tuJzIvgazaZPvzA1G2kRTAYILePK8rygd7WDJ73eAx73ccdg2QRy+G5n30PP6ArhrCnVVOgWmltb8y4r5KcEN8+YBzfBn48+2Met31oIZgQaeF7/QJ8t97P2APTR+i8Pvf3XLf9BQCOIpdR+QP5wHD4w7I72acifG301wiZny1U3sPDw8UTKWkYOPASyitOYu/etwgGq2hr38Zja4ZhBzZTceE02sskQkPgT7MQUrLDHowYJlixIBf5sUQKiUQihJupsri8nektExnsH+fKE9GZxE0DZmuYHKtLr2pK+ugIBG2UBtsxCEd8KL+JoxQOYGvNoZEVjLbWIcQkN0ZY224WWu0ADkIrpA7iyyAwlJBpRYohBE6WQwKJpHdHHnkLH8x+EcOwWLjwNvz+XAw9DVtlHu755qNLmDc6BIbgEitI34Ig7zxwD6PWj6MPlTS+tpnKiyen3UfXUaXmxkYAdtx+G3Nva0R0S7OuOyKnfXv3Mvngmewp68upQfBrSQBJQBoETEnAMAkakmuboXbKDMYfcxROLEo4GqWlLcwHS6vZ3lpBwb6e0UF+DTrFTY45MXy6d3+SCQdkcz8rmRX3p7nx+a9y/XndM8RGo2EEYH7GulDS0thGeh+rGOA3PmPZCyeG3cXZuTnsZiIOHTCT7133EwBOeva1lJZC21EsqW1hUp88Al1ywPz5iGn8uUu7TzYvg3owje6fwerNb/H60nuosZopMHN4qs3ND/PsjFsZMmgmpi/IhdEm7lx6J3cuuZNHVz/K5ZMu54wRZ+CTn2+otYfH/zU8kZKBYKCSAVVfR9uKfXds59f2pfjaK9Cb4Bdj1xPbU4MIhLG1psG/Cy00jTGRSBGGFh1ztBbXUV3QwD8uvbjHcTY+8yHBhZ2dVzP1xCJhJub6OX/6mWnPcfajj1KTN5ChV7+ZutF3T874dOyGIKcWIaZwrS29wTT9OPYsDPNt2trvobUNSnbm8HHsFBhzRNptjxpWxjzcjv6m40YDsOyNlxk1ZBwAMpKhXhHdM7bkxC0IlXv24CvQaGkk9dPJa2jgG689R9URM7ji+NS+Ez95fxn5RaUcN2UEr9//a0qnT+Oso07lm6fApNvfw5+kIJ5Pg0oRfhtzYpi9/JPML3CtY/NWPMzQgZ05Xc4+7Hp+v+0lWuyeDqDRSIQg4AtkrvabDmGDY6S3iFmA3/xsBQZ1ezstOwMse/A/SCFojIuUfoM7I9+WtxVgbG1l+Ko345F07nLHclAx9xxlgY+1P52FP8X9t5UDGJhxq1l7tJVlKx/lihV38ZXZFeQrg5gMc8xB+Zwz8XRGDD+hY9vCQCG/PPiXXDT2Iu5adhc3zr+RV7e8yl+P/isF/oLPdP0eHv+X8URKljS9uZU9Q+6itWIxfdadR8m2E7jt5Afo0yc7/wiAbzx0HqEuFVZjLW2su/stfA0GeaIIgJahbYy65FiqTIObLziD/EDmxG99WnfQp0tBvGS0RTTKiRFtasSXm+c6me6HbZgYaUKks/FJScZxx93L4iV3Ult7P47pFm9bvudlLp5dyaWjT2ZG+fCk270UbiNh41+6dCkvvPACjDmQxfZmRtv9YFs05TG1dn1Dup5vKOia4Kt+9UumpUj9D/Dy7//IsEceZNaIoWmvy9aQ29LM2jFjGQwsG/IMF135Hx468SEcR+Mze3aGxVpgNyTP22t/iuGe0qJhBJVmY2t3X6X6+g0AfGTtH38GsWiUIOD/jIU9hS1wzDTfCK2Jic8uUvKXb6FpTg5lc37lvpeStROGUBxPvgZQVl1Pk2MyYngpOv4VTlgrd9S2YbVZWM0Wo296i9XXzSLYRUBajuKRVbt4eX070jJ5bt8/+c+C3bzcvp2wFEzVAfLaOodvprRdwKEzrkh6rgMKBnDLEbdwzqhzuPLdK7l29rXcM+uez3T9Hh7/l/FESpYUHjuIor8cSTR3FzUFYyihc0gjG7RtIyIR/H43THH9fe+Qs8lPMaUgoKGwjoHnTqdqaKeTo5ISZWcOWR5Tk7mqb9/1dQyqa2bzdLdztqXAMQx3Mg2UaXJTsIr7x53CY4ttELA+7BaJ3FDgBuxephSXa83ehe1J7S1CCGr8IZ4vP5pH+n4VA43UGgMw9FQMpiAtDbgZdBdt+SeLtrjZZMuKD+XBo29icF5nGOfMvBxW4FoCXnr9jY7lS80t6OYaRs95hX2DNlB6+eU9LSLareKsuoz3KMeJn2d6C8zYk08g+siDbH/634wa/+ukbaz4cJVeafHuzLsprl9D2NjBsj2vQLQF21FJRQqksaT0UqT85ZmzuL91PUjBJTNu6LauuNgVWC1S0tS4lcKiwZ3HiVcu/lSZVbsgbbD8qYft7GgzheG+6K0ONfPmIUwDGY/Ckr5EFJaJNCXSNDGkxBfyIXymW1kaAUJS4YMGPxS98j5aO7SsXgUP3kF5QadwqGxuZLDfxwsXnpj0XCxHMfHWdwk3Rhl9/RsceUgVfz9+LMv3tXLxgwuJNMfvCfDoiGqGip2cllPFMUNPYcrEb/LSoyeys/8RoDW7t6RPCgcwtWIq1x50Lb+c+0s2N21maGF6wevh4ZEcT6RkyWM330y/XTmMrHNDE8NmM6t3PshxJdem3Gb5m0+w/aWnkJbD0Pc38jNgw8hGPml6npKaUtqdFuT0fPodOYGqPkU9ttdC4jiZMpPAxvJpVNavJl1w8yB/zH2yvPRiVDiMbmtDhMMY4TAyEoFwhINXrGJB5Vg2HTQGpTRsd7etq5gCuBEusr2VvNz4OemuL+7/wdZ1HF2/gI3lp7g+M7jOtk7iVcAWoChvPKaZw77GjwHY1/ARpz4zk2e/8gavV3/Ct0bOZGKffKhzRcpT449l0ayx3HKLG/7buu8TdPte9t7xVwJjxpA/c2b3e4c73NNVpDiOO2+kEA8JhkyawGohqHr6CRZPn8bUk0/o0SYSj07q0+QKn4aSMcAY/rjjLXjpKpTzNfxJjhNBs688uQXDdZzN3ofh9eb1IOHq8hkMqJrebZ3Pl8OFOUN4tH0Lqza+yqEHfq9jnR11O2TTp2hsWkxe/mQ+bmwlYBhMKcjJKi8JgGlLojmpv59tdTv56vJr2YfBM0vCafcVEHBsgRmPLrNBRbF2ryGy8B8A+IsEFQPcBG2NKxYB8OqzT7B9wTyOv+0uolpSZKTJdGxI1vxsFn9duo073tzAB3N2MHbOjo71h0zrx+6aOrZuj/LIrDs4cFD3ekhHnHIWby7IYdPYPSwadAuPPuH+Dmg0KoUTeJvlDlWWhXrrKuzh4ZHAEylZoLXmX4UHUT2pgLEN9fw8+CsaS5sx9ileWh7hyJGXUxAq77Fd0023MrzG/XFuCwpyI5qhDXnk7M4lRhQ9McDws3vWhuk4rpDdQpBTUV8wCDvawug0bXJ8GnNiH/r/JLmo0pbF2gkTOe2gwZz0Tbdi9Y7f9GNn+UwOueDvGc8hwYIHL6CgZScPnjgzZZvxDwcZV3k498z4HrXhJt7ZtZKb5rgl5M58/ngA/rHQbduny3YzHytgXNVEnKZ2pu7bQ8ww6HPKKeQmq7AdH+7pevdU3MlUZshqJ4Rg3aSpjFm2iNZbboEkIiUct8oUnjGQU3Uum5fuofHjtzg42goHnI+zxukQKfe++F3KfAvRGPSZIQlKWPbOARxwTPf7GlOxXllSbK25rGA8F5+YfDghmEjNt5/zph1zKymv3eBaHZ7jLP4j3NDZR8cMYFZldknJTMdI6QQM0G4WI9lD+WF7OfqAMSgrnh3YclC27U6OG97cWg3GBh9iaITCUovmxa0YZSPpe9mpoBWBidM69jtwxhGUv/I8tXW1rN6xhW3nnkZ7vzMwZWar45WTB3Hl5EH8dt4mXl++m7H9C/jaxP4cM6iU615/gq3bC4ioJlqj3YfJcr73dYaf+Cx9d/2LkZbg/ubBHet80pdU2LVZbfil3/NJ8fD4DHgiJQuqr5vDyuNdETI3vx8TZ2/hg6NPQFgLyKl7iFveeIwP9xbywxHf5cyZrlPsmnmv0icuUKwHbubAQ09n9nHHIep38Z/mF9ywk/djVDx3O0Gf0X34JP4mpB3m7G3grpc/xG9ITK3ik+72em4YLm9aQ/i3A3BwM6Aq4cOR7quSPtq3R2HHXlIFIWvLdUbUPYawsk+65p676AitTktcfJWHCvnasBmcN/QTntr6MZtaanl/52xq9nZGpATtwUTMrbT7mlnIHCiDJRfAob6DuPf8m5NamzQgzRhvbF/HkZ+0cPTEI3Hi0TwiixDkgZd8E65cRFNJ8g67KV5pMS9gMnBYKQPHlcLpJRD8DvhCnDf4YobkbOfhl3MYENrIztZBBHOn0NbWSqDoNerEm6x4/ccI6UdKP1IGaHY2YKQrQ70fMQHBFJEzq9c+zz/b3TRqE4d0LyxoxWKAj2W149ncNIhYbi7fjz7KhoLBXLT6YLZXlKTNl5PAdAzwpW4Xjd+jnAEVlI6fmHZfNQtrsDdswJw6kbypFex57CcERp6OGnASJWfP7NY2p6iIr//tATY++TgbP3gHy3EwHE2Rk/29+/Uhw/j1Id1zGbW3VQMFNG74Jgu2pPZ3KjAEb3/1bfcanRg/W/QoWtvcOPUbLLz/JUZsrWT12F3MHrqCFXUrsj4nDw+PnngiJQvaD+neEfi0w/RN2/io3IcZtJCto2nwb+Yv6+9h1rQzKMgpIvbtH+MHAk/cw5jJbhr3FrMAn9mOCOWjgdErPnJ3mJuLv3S/UvdaEwnmUNnSzAGrFlA0aAg2AksIbCGwkDhCYAnJxzmHMDivmYGtdUgsDG1haBuJjUEEQ7fG95n6GhMiRRmdT926l5E8LiJtgcFEm/1bCCE4b4g7ZPGLiafiqJt5ZvtSJhZXMbqwkjXbNvLDOVeyy+400W/nY9551+1oVMtRaNGMJJ+ph/6SQLCU04e+xuMbT+eSx1s5dcmDTMovgyyK24XDEXKv/D4Auc1NSds0We5Te27XfeV3OlEf0vdjQjLCzsgM9sUO4LCpVzOgsYKmd7cBX2Xp2B/RVLoYrWyUiKGFRUswinLyyZYo4DeTi5RnF90BwGvH/JO84sHd1jkxV9Tduew7AKwOXEyOcDvlS8f+hoiaTG4W/lY+x4AUOV/ArXgM4Muito+OCxrpd/eXiERrX2Rg732ZsotmIXO6+9AMP+98hp/nWoD+9MNHyPNn7yOWjInFY3gBEH3OJGa4kU9dy00IAXV7X8EUnQLmjeoVvL32dtBwwJIYs7a71a7Hru7HjuqttB04BA8Pj0+PJ1KyoOrEcRT/+3SuqN/HaaYb/hrYuQpR0Qej7TB+d+FDHLbgP1yz5jf84MHzOGdrEUPjD/dDJ3fWmdkR9VHgK+H62/6AdhzWjnMrt7bdchcHnn5Mj+MCzL3xehYsfo+rf/qTDGf5rbRrq989FCp6Dkkl0LZ7wl0tKVqLjPV+epKlJSUDhjQ4Z3BnkrC9xq5uAmV80OHkws4oGZn/HloZCOmw5JP3ATh6sGRYqeDxdZN5ae0AXoq3VeHWtMfeOrkz98rMN19L2qYlPtyTl6IzD8kI+0ou46IDOofXNj/4LFbJHnLqx1EzeySn/vPxbts89PyR+HtReDEmIGCkcH7VikolqNrPVwVAxWxsHOb+dCYzbnm/Q6AA/GPyOGSWDuF+5UuZmA4gasWdUbPIx+LEw4QNn3vsQf+6kepffIAwA8S2FVLzx5fpd8PZKbe3EWRwNcpIzAGB4rjx1+LzJR+ieezd14jRGbqd73c9wV5b+7cebY9vOpTF6+8nMrSR4LCiz3ZyHh7/R/FESgaUshk6dwPP1lpMbtwB7CAiDBrzTJQShEJufZDjp5/N7KWvc/xj+5BY1BePpr1/If5nP8TwGfQ/6oBuXbcwDPL/8zwtZ3+Fmk/W0HrkQeQVJflhFAItBJteeBYAw+dnwDHHYvQ6fFQj7QiR2h2YecUYodxu4+gdlpQuT72u8+mnGe75fInaUa54xw35nFo+lX+d+C/++fYMDN3GqIGPklPQh0h7HWtW3Aa573WeilSML+3DOz+7nPvefpIXFu9Fyyhs2UjN/K1ulE8i0kfAxpZm9t5+G84AgZKCujv+yjsb3sNWNo5W8do5bs2c1S2tDK9u4b36obSty0MpB601ppQEfD6K9g5E1r/ECnM8gUAura1LWT/5CUwzRs7C02gW0NLejN/nw2f4kdJgS3trR5XoTNhWBFsIAikym4ZVjL5mTtJ1ynawpM3g4lxuOmMCJz7/B14LXAeArBif7ceCX/mQaawXsZj7ncqmDIG2lFs/qMOS4qPq1mOoufk5nKY+qGj6nyoHia8XQ2VJz9dW+KSFTJOATWgHp0tdrcvmPUCqv8SdlQ9yyaKP2frQ6ZR//zKKv/61rJ2SPTw8XDyRkoZweCdz5x8HPM6ZB9zBY/qsbuvtepM5D89nzn2nABAoHMGKid1rd6x/0wIseGU+uX1Pwtf2Sce6/JJCmhAMe+xu3tm6mdPvv63HOYQKiwB4/vEHOpbN3LSBqT/4Ua+uRRa1kbO+kS1HHAeAFhodcNOgu2lmBRJ4cMFOflzrJsR6xYFRtS+z7dZJSDRXF2vW+mCA4woYDfGkdZ35SBytmCL8PHfD625KeiEQIp5yXYCQMYL9wny06R6mbLnfPZd4tlcd/9dtfj/+Pst1NvWbOdixeqqGJzrVCqRxNdu3jKai4igQNps2/JOBk04H4NuzzuOsyrdYWns5DUBD+1tJ71P+j+BHO0Ju+OuC1Pc4L5bH8dXHA3U0xK9fC4GIF+zbjWtBW7Xy445tatXZvBob674pncadv/2wY50hHHJGu5aHQx5wrR8CgUQg4/8MLTvmHWGBAYElDxPe05/Q+Vd2O7+wsshJJVIsV6QAnD99ILPGXMIO+5sMKEnePhna0fi0mZVICfgzJ41TtuOGqnfZnxCCvtedyfYrH0Pmpt+HIwRm781+3YjaCp9hpw1Rb4rWY+EKwwbbwd/sJlA8bdSV/HTXJbxQ7Irk90f+gmevWUxTIJcdxYN556NKKle8xNE/PobiytzPdJ4eHv+X8ERKGmr2bEPoKI9yFgcf9C7a+YC91TvZsucCAEIlXZ3rBEX2GLQvyvk/GAn+ANrR2FGbpU8tZlNDCW15/WnLKe7YorBfJW0vvsrus8+icPlCVs5exKb35yFjUUKDBzHr0q9ywPeuZMChh+NYFrP/9hd2RFqzivjZn33n+shpUgzudyGqrQ2nvR0ddgu7JSo4P7GrD7smj2JMQTEazX9aLuaQ4GZ8QRMbgRNdBtQzq3CE21WKuLiJp/2XQvBiw1bmBoIcOLYcpTWO1iiVeAVHOeyOn9PQwqFu6YD9Jp/0YQgDU5qY0iRiR2ixWrj32HsJ+dwOQsoAgf06k8oBY6kcMLbj/aCR3Yc6SsYfy/SdL2C3N6CVAhRaO6xtWUe41RWIm6NB7j7hWXzSwBASvzTwSRNTGPgNA5808AmDj9fOY1H1PI4/73gOHnVwxxOy1pqIZdMSaUFEG4hEWonF2nnohSWEmlv4xQGl+HJCKN8ujBwby3awHIeYrfjrkm8wpGAbZw8e7uYo1qC0cicUtrJRSuFoB+W0Ear9NweHI4jts4HuIqVdO4RSWASU5WB3iYQpL+h9vhQr6n7304qUuHUum+EeZekeIiWBMPxuqYc02EJiZsh+C/CdV1fw3oo9Hda+rq9mewyftPj3hyej4qHgTkxTHNGY8QxxA3MUn4QjnHTfGQCUGpXUGzuxpM2NVW64dDR0EOe+9jRzRyaKSNYR0FFqWvJ4/IYFTJ4lOXQ/Z2APD4/keCIlBc3NzTz26Msccqj7A5abNwAhJNIoZEtn1Xku+vMDbFmymrULIjTX+fGHTErGd48aOOGAIdR9sonH/rmBFn/3bCb9Rg5mVWEZwbYWjO98nZGAJQwiph91ydlIKekzZSoAvvuDEGllwjd6ptXPhMqHaP4wBpzxs6TrtVY8895bnFrq46ZJiaJ407q1eXLOXazf/DBXn/VsyuO8+fSVROyt/PucKSnbTHggh+mlZ/PP067p9XV0IAIYInv/jQR5VT2HM4K7AoTXuvPfPnFVVvvxxXViTrB7XhEhBCG/j5C/BOjiDO3fiDTCfPu8RLj0pB77fGv+mwyLHczlp6UOS+/On7BuPgInyRBRWNuUpIj80bbGkZ8md3AnsUhCpKT+Cem0pGQemtzfcbYb0g+6Z3r/rjT486jXqX2NtHKINtWycGU1VoPD8JElHdbAhOQP2U1MC75Ki38cGo2tFNWOxWhH0dcwAM2+sEFDWx59/V2D44dRyz52yl30zf8JOXIQOvddYBUCKFRlhLWPUH4DkTY/8599gdotczjigm9S0q8q473x8Pi/jCdSktDevoUVKy5n0gG1Hcu0thHCT25+PiX+31Afux67vZKyqnJKKsvYsX4pzXVNxCLJn/hKJw6jrXg3tPVcb808lqp/30/Y8BP92Q00bd1B5RP39cjnEQiFoAkeuewivvXkC70b3xaajnzhSdDaIkwOeYaVso0UkkwhyUqrtPV/4ieDUp+tk0T6MT9Vkv6eHFB5CO/FRUrMieE3Mg9PRC23kw76s7NCaOWkLICXIKI0wQyRRz1QCnw9/4zDWpGTyqnWVp+bSDHSiJT2D7dw+Jy/ED3R4RNhoKVB2BfloVkhVgwPYWAg41OOI5mg2zn1TIcyWeaGiUsDISVOu0JHG9hw1L8Q0gApEYb7imEgDIOr9HC2VU7nt7+Zg5ACabhTeWQTk1reQ2IjhcND2GwqyueMS+5LcdadQ7q7WiNMWbiWm/oUccyMwR3LL8ji/kSPHc1dF89m4KTjOPu673Ys10qxZm4ec596hEd+dhVnXHs9AzOEZ3t4/F/GEylJaGvbQCS6kRHDz8eym8jNGYaUnR3X5MMu5P1XlmJrN/Plgpc2s3ujG6o6+diBKffbtfBZV0763U9o/fF38AcDLH9rLn2e/i0BZbup9Ls4sh5/y+3s+PrZNEnFA+d+hdxB05AFBQgp3UkIhBSuxSe+TBoGUhqo8ghGmg7QcWKECZFvpDarZytSMosnkdTfpDeYaOw0oqs3SNl5j1fvfpsDqk7KuE00PtwR9GUnUpRSbgxrGtqUIre3YbTagSTROO1CE0rhVIujUVkMjaQjFouLlEDqoZyC3D347HbCJ12Azx8C2ybntX8xuqWE/PwjsZUdd0p2ENEofXa8TaBeYo4Y7Yo6pdCOgzDbIBpBtUddUaaUO1yntTuvNSdYa1mtAmzKnQVKIxQIrYmqYj7Rp6C0gYOBrQMEfXtSnnNXEpW6DaP33zMrEgEEkdbu1h0hJWMPP4oR0w7hhT/9nudu+Q2nX/NLBk9MX83bw+P/Kp5ISUKiAx027Gp8vuLkbbRC43b6q+e6XhbTTx/KgScOTr1jkTqHSCKyZ9Nd/2CSFUkcpFsbIxjk4gef5OFvnksbmsadq+IhwgmHU4XrdZpwOk0sU4w6G6xI6o877MRwhElemjhOV3yk79wcreMuuOnYPxds7xHSh/wcQp0TtBacRF7zq6zb+QyT+p+YUWhVr60GshcpWqmMlpR2rcnz9VKkKOVaFbodTBMGcnwpRIoNKk0K+Wyw45YUM42/iZnvfo9HXv9d8grdpHjLXv8XIweN5arzbujR/oV5x9FQ0cCUf9/b6/NZOXYiIw+o4KzfzUzb7jc3z8PHsLRtEqhEGYVPEZHzzoPPgnY44oJzkq73BYKcfs0vefHPN/HsH67njJ9ez5ADpvb6OB4e/3/HEylJiMYsFu+ZyM45b+EzczCkQEqQQmAId357s8KKVhF+YxORVneIZMELm+kzMJ9B41KkFc/it84fC1M9+TBmPZHcHO3Py+PS/7zCx79/nIU7Kvne345CZBHV8MZLh+PPGZByfXN8+CI/TY4MgcioLbRWmQwG0KM+ce9wrTU+DKGw7Dac+BO50ja2tlCO++o4FnmtFqbhAync0S4pXLEodXzePZ3ceCRMWfv7fFL9KpOqTo5fj2bv7iZa28PEnCiWtoipKO2mG4mzaeV2agJ1CCkxhEQYcQdgKZBSdkyR1jCRWJRtO2sI+Hz4/D78Ph/+gInfNBFC0KY1OVmGIHegHTD2+zN2YoSFIGQmjyIRjkZ9trxnWFELH2AGUg+NOXFrSyDknofWmoANRjC5eJIxG9VbkRZHaMctTJgBrTMatDrosKTst0FjQwvRthgVVaW0NLVx/98fYeDAAXzlvBORUtJcV8/6RS/gD5UxcGzqYoQ+f4Azfvprnv/j73j5Lzdz1EXfYfSMIzGziIby8Pi/gidSkvDOmp38bfmlsHz/NV3d7NxQXtatxVcIlzUHCWnB+0t2cVEKkZLNj2PQiqJS/Ih3xXG0+8Ocbdil0AhSdwCr99awcP7XKP2wgSbp72Hr0ELQpyCIKAgy8aGJKYdrtBYYTvosm3m0saj5SU5/4Kn9juEWJGxD09CRviT50NDheRZnFWtmz/58xvMTd7HGKWJaWafD8Kr1G/ng9h092od8I4iVLufNt1/Jav8FQEm4gf/8+NIe6xLFEH9o2JwtVqGuV2hhAAYaA+LzbmVgt0IwwiSmg9ixVgpr/03tjVvRwsC2wmjdQstQyZ93v8s7952GIeKBy8JAKs2va68CYNevn+wUalK41iPpDklElUWLBKO42D2clK4viBQIQ9C8u5kKSlj0/N9o7jcKaZgIw+h4FYZB+9o1KAG+uN9OY617H0WKHD8y5uB8iqyxSimkVuhsMtuiyTaTz5Zqd1ho8cubOTZkUTZhKFvXV/Ovx90HCFOHsIVb+qJxfQ01t9Vi2xb14V0w8gAirU1ordNa5aQ0OOWqa3nrvrt54547eO+h+xgx7RAmHHMC/UeNyeo8PTz+/4wnUpJQWHIy8AkLfnYoPkPgaOWG0SqFoxWOo7CVg22F0MrAdhSLFtXQ/m4N9bXpq71mskT4rShWFiJFOe7TY/Y4afM/DIntYUC0huX9j6C578EorTsSucl4Nxrc+zZQz4z+M4DOlOFdf/TfWd2IEZuQ9kyuqmthbX4xuaXdKwkJBKYQ1LTV8grNDAyV079oGKY0MaTREZZsCIOAUGw3WykLlSGlEQ9fNhFIDGkihUHRr+8BDWXf+14iqQsoEFqhlUAojVYaoTRNNdvR979B7JvHkH9sZ+TGrr2u83TB6U2Ul5Xil3780odP9sG0R5Fr5sZH2BRO3FdCKbcyrlaJZZrZTzyIsB0GnfcDHNvGdmwcy8ZxHBzLQm//hKb1q2gqOJb8qkGgbLSyEcqJv1quf4a2QdmgHZpaJGvbDmVk0XJM0wTtEG3dgZAxiOfyqFdNKDQqntHGti1aRBv5Ohe7OT5UJdyMLJq4UEGgRC450kDUx7O1CIGgcz5ECSrSzKEPfQzqo6Sfcz7QlNf5nZv7g/MZBgRK+yRtP3R5bdLlmWhatz3rtg2WQ6AxlrFduK2Ni/Y2gpQ05pn8+6/rOeKIrYgxndE4FUX92du4h759qigtKWXJ+jmgYcygKWzfvIC2vELee+Ulph1+BNFolNLyiqTH8odyOPnKaxgy7VCee/xRPt5azcL7/sGhhx3GMV85K+k2Hh7/V/BEyn5orWnb2gJAaX5hxjovCRbP2wXA4bNSO84iMo/4BKwosUBmPwelQPZGpAgFaSwp/XyuaX7okReSP/LcpG02v7qTnD1zOxKqJeOgxY/Rmsb3BeCEtgiDyo/nkK/+PvlxNr/FKx9ezY1jvsXkCecnbZMNK1e7T7wDjr4qc+PVC2n5y5vY1dXdFre3uw6Qx047jMoUnWs2zH+wjfyy/px1xvFJ11fP9vPk+lUUnXwRgYlHJm3Tg7nzWPNImMmXnkvxODek+c/nnoI/p5gVFz2SdJPVjz3Mq7Pv4uJf30zJuOShzlvffpNn7/srRUi+9dSLHcu11q4Ysx3W3vpHzOcfp/ydl5F+H8q2cRx3qK1j3rap7NOvY3t/c4SdA3M45qwrkx32U9O60bXQFEzKbHlozpEMrk0dwZbA9Ps5dPd25vQfzOgB+6jY3Mz7c/vTsuJdRNDgiiuuoKyie72t0reLKe9bxohxg2hqOIzb7/grsxctYfaChSAE377kYvoP7mllDLe28vzDD7B+dy0U9aEk5Kc5arFk/UaOzmCJ8fD4/zueSOmK1jQ99gHNK90IF6MXGSxrNjSRD8x9fydTJpQjk/gWCJHZp0OgIQth5HwKkZLOkoLtWoBkqogQwFF2GpnjsrehKOOpGDggU3/1jHgSMlulT+CViW3jypBKk02i936jp7JawL5VS/nuS8e51getMbYMYwLnUpSXvJZLtjh2BH8o9b212lxh7MtL7qidjFjYFZa+kCtqE0n+AjmFmTdOkxBw7auuMBk5pvtQmhACIQw3gVssikZT3Hdw0u96MkzLoW1kVcr2+4ZVUVLdwMYTziOiLJpEBBEw3VDjeLQaUuLX7RSrfQRDIYQhsRvikVYVKXzBujB1QBGx7aktNm0tLQRCIXw+H0fl+Jnn2Hz75KPIPyeXD3//LHPbbQobW3sIFIAZszodXwuKipkwqIpN27bTHvfzeu25Zzj8qKPJLyikoKSE3IJCPnjpOeYsWIjt///Ye+s4O8rz/f89dnTdJVmJuxshBgkhSHAKtFhpoUgpxV3aUoo7xUtxCRI0CRDi7m672c1m3e3oyPP7YzYrWcmG9tPf5/PtXnmdHJlnnnlmztmZa+7nuq/bRVp8HBdefgWxcfHs37+fDz74gOrqauLjj71fPejB/6voISmt8dWNxOS8yzVOB0+G/nlcdzATTslgz7sHUPfU88RdK7jpLyfich51eDvLQW4FIeiGz4ideSAfh/hUwkKSOv+6hdkUAu/EAAxst9hj/WDiY+po8Hdt3qXQdaaLLNsndeu4prPaQ0i2PUx3IMsyRoTEqDzB97VVmG4NWZJINiQsyeiWtXtXsMwQDnfntvO6/whJiel2n2ZIBxTUpn5tEuGkoTKf1/9wDw63l/PvvJGIuBaCJTX504guBNJmMESkBVMferjz/QkGbT+S4xD6qmGrUz0KgJJ5AnI4B9ntorGunJAUxutUwTCaU4+xLLRACZLwE9K8YFlYhoEnKYT/m7s4tCoTSVaQVQU1awwpp7WNxFmW6NQu6N1vf+QuZwxRfj/jfHXkak5c4TDRUfbxm/7Ahey77S5k77F1M5Ikcf6vbf3RvDdfZ9fhIgrrGvlwfktkKsII0qi6cHllhg5dgENzs2rNMlyunZimgwkTVXJyI4iPP1Zx0R704P9d9JCUI/jkctj9JQCFjmys0DHaH4WTT+zNpDEpvHrLciLqTd68aTl1cSqX/340vdIiATBECEM08odvXm7SUCgokoQiK0iSjCopnCLrCI4dQTju6R7Z6BZJkbsgKYYwOVbmqscdwqLrOX8PIbzhik6Xq02RFNM6dli+KwhJdJukWKaBGqWgFBnctyqGoa8sBmDxRx9z4FDoXw65CyvcJUkJ+20/DUdU9++azbCtPZFdLdODmSNPoeLQPvy1ldSXbeetp0rwDklB1RxoDify9p0AHFgyn4r8rcQHanEUF1L5wZfImh2tyItLJaSqzLthLoosIysSqqJwwuU3Ej9yBgBWMGBHOI4DathEuNqTvbAeYuPS5WhJYygZNIST/3Axyx97lVA4xO/ubz81lP/S+UTU7CbhPlvZvm/ROqKXX4OLPVCzD7BwiAZCpV/B0STFtBCdREi/rGnETE+g0eWmwNfAwfgkJhUcbNOmOMI+1r7KSrwJCd3a72HjxrPrcBGaMBkxZDCb9uy3+1CcTBk3lvR+JsXFH2NZMlCEJIHDcQZQRGPjK+zb10hk5DA83my8nj6d2iL0oAf/L6KHpBzBiItg95c0pJzALTVXAXDLmz8gG0GijXISvEpT9qpEUb1JWdRQFEVFkWUU2S5upsgS5vR4KvfUkFxp0LsavvjLek4cqaMoEpElYRqVepZUtS/rfgSnCwMpbxW7Vn2FJMlNDzttVpJl3CEfSaqHmmoDvxZLzd69dtaQ3NRWlppeS80mb0gSkhLENEKEgjVIiowQErKs2RkfsoqwmohFF0QmL1xD5TGMrRQZDFOmoiGEU5NxKDJOVW53kc+qWNpFH/adqtGqXothGvj0EEEjTNAIEzYMQoZOyDTs15aBbhj2e9NOSQ7K/Yi2Itj/1tfIqgqqiqxpSKpqp6vKMlgWYX8pNY+/gmrE4qECqaaFoeZvjsYMOyg4sANV1VBUFVXTUDRH03uH/V51NEeAjoapGyDCOD1dRFICPmQsFFf3i8+ZYR1wo7pb1jn/7msB2LR+C0uffoxwTj7hnPy2x9e0iHjhPYQQVLb6PHJYDLLLQZouyANcTtUWAVsW+0oM0lYvaCYpIhjsMhpzNCwjjFc3qHY522W87Nm2lYzFLsBFfbw9FeNV3JQYFVimhXzU9KdkhjGlFn+WgadOhFPbpuIVvnwLscWfU7NrK5LiBM2BokBC3V5qZBe6YXLjD7sJm4I7R2aQnRrJSR6NkrJicpPTuCUlmhOH9sI9rmNPlSdefJEz+w9g3K+OrZkaPHIU54WClNU1sGbNGgCSkpK45JJLiI2NZevWRwDIznqMfv3Oa15PCIuDec9SWvolhUUtGqPU1AsZPOhvPVqVHvxXoIekHMGgM+ChOl576inS9RL2ypkszNGxBEjE4ZLCWEhYws558VOO1YVpmRIBN9W5UITEmq323aOMRnJ9ATuu2IEQAlOYzc9HCslteHMymbuL4Td3NvclWj37gDygfMZLAHzwbHG3dk/z/gXdl8Cmb1bx/piHCKttQ0WyEMT1TufbN848Sq/QciK8W5a4E6h5OMHO9xGCWNPgPc5ht5KOJCROtFR0y8HUvy4gSFujL5tLSeQ6YHPsHGZ0MtaSOpuc3Lj9dcztb4JkIEnH76tybdRzNERB4brWn1pAuOlxBFEw9g4AzjxlB73OvLZlzE27//VTnUd+WhpbSJKJJJtIsgWSgWk5sAz7WO9c/C57tyxEdThRXS40lxvN6UJ1uWjYvg1ZEuR8+icUCSIHTidh2IwuN2eGDSRMZGd7rYupaLhirmHMTdFMGjSGsB4kEPDhb6wlXFyEGeXks2V/YnNDAW/PeBE5JgGtr52Vdd7RnQnBUxfPZcnSXQz/VSVaVAJWKNQt7ZQRqGPrNxdQF38QngQ3y/lpST8KxCmMy/wdXtVLTUk58cTgunUAMxNtMe+A4YPYteYglfklJPU9KgvMCmN1UjyxGQ4vXqUG77y2IuTLgFJvNlcszOAnL3gNwcIDeVg5EsS2bMcpKyQktY+U/OHyKyjYtJEvd+ygobbmmPsPkJeXx6JlK/D5fLhcLs466yyGDGkphOlyDbKH7Ehpu5+STN8+t9C3zy2YZgB/4BDVVcvJyX2MuNjJpKSc1a3t96AH/5fRQ1KOghCCC6aP4oXp9slt0av3s788wI33PwnAW599xa93XMairDs49cp7bZJhCYwjD8PiwMr1uP74OwQSqrDImXI6sx+/n9duOIm0whBwA5IkoTZFLbRWF/Phn3xJRf5eELbdt7Ds1FFhWRQWb+C1g1/xt4y5/MJTQo2URESC03aYtY60pckqHPszIcAy+farWHr1zUPPdBMOhJgWNYaxvTOgiSAdLtzG5w2F1GVNxuOMs03CLNN2rG16HdAb2SZ8REWmIwGOcJixB1cwhY2UDxqJoihYlkptbSzBHI3hyS76pMQRNExCukXIsKv9Bsoc4OxciJroiefByio+85xGXO/hOBUHDsXR/OxQHGiygkNRURXVrlSsaGiKikNp+lxW2bCrHLdf5co7x2CFDYQeRuh29okI60CTs5cs8/V7OehBJ5FT56K00k1c9vBJFOXuxTR0TN3A0HVMw8Q0zaYsFrPpvYUwLPtzy8IIh6iuXo/fNwyVZMqrwZWQguzxYIaCBOtq8FWUIQwdTAMMiVS3j3477WrMpfvnw7AtXf5WGxsECgrhRh3ZISNrcrNGJNDkCOt2uZAkCafDjdPhJiY6AdL7AaDucNGgKTjHntT1H4UkMSw7kp15Dbx+y704vImkH9hPYieRo9ao3b/cJihNKA1KpLgEGdIPxL7xK6CRfsRgYuLxtkSEDh7IRRMK0Wntp78k89gkJfWye6nefgpCD4KpI/QQoYpSKksW4Q7mscYluEZ3ctugJF7+NodGVWJQzhYaZg8jTpGYPfWEDvuN65NNXJ9svtyx45h6nMbGRj755BMKCgqQJImJEydy6qmntltPbq6x1IWYXHETGTGIyIhB1NVv40DOI8THz0DT/jVBdw968L8dPSSlCZYVoqJyMbpeSm5uLtObSIqwrDYy1lPHDoQdMCXvOeBem2woEuqR87UTIm66url97SPPc/rck1A1lYSoWCS6jnwkpGSTkNKxGVpwQzE5IQnP1NNIzJxGdxNiRaAevtpI/4xE9LH9YCWclXEVp45tuctcJb3I57tfRZr9MKR2XME4telxBP6GWngqE6emcmFqOr2n2KnL6/ce4u2cnVxzQhpzJ7VPC/U/KOHSOj/Bux0aFzT4yB41h7EzL+zmXrbHZs9KAqZOXO+YY7ad6Qqw8MVKFry+gl/d26f5c01zkjWofcXiYyEYrGDV6ofweoYyadI5wDldtrcMg8rNq9DnnQMyCIcHs2AnkuYEzYnkaHp2RkITOajNjeWMGI3yv9qhIiEEJmACKQIivQreLoSqYTOEo5vGZn5rOrK2DzAJ+WpRJQ+WcmxNlBW2rfFHJb1E/LA5AKxcMANNTyF0USx+PUDQCOCI8pLpaakQfqiqiCFJ/XC626fj927YDEDuX0aDEEhNAnIJ+3XHe2RHQLOsavLUVNYM709KcgSHb3qSX7knNjUZSvKQwWipneuCgpWH2LPoRrL7mVQpbn5akossu1AVD4riQlGcyLJKfn6QXbtqEULQu3dvLrroIiIiIjrs84jP0K7df2DHzmQmTXyGaGcaZtiP5FZxuNsSkQH972Xd+tPZsvUyRo18E4eje9qYHvTg/yJ6SAogTIvC4g84cOBhBgxMpqiwxetElO1ApuV9WtZAKoglTqrh+wObeHLdaxjCQAgTCxOBhffGITz9wm4Awnu/RZ8zEVWLIsoZjVOubLf97kIvsgsanrH0Bh7KOpfzp/+5W+tZIbvMveJwcthfCsB9O25H5klOGTsNgJ1rnoZoL93y7m+C0xNJXvQJZNetgR+vwT/oBDwJGZhH7MSPI4W7NcSRzB/xrxXBk+Rjp3wfQXVJIeDC+2/SJAaC9QA4HB1fmI7GljvvxPPtd1RxJORfTe07HRA0SSDJdnJU9uCLMLOmYU7KwDIshC4Qugm6RaiqhqRaGcXVhVjX1HFI7aMhZatXcHhLvl1JWJUBieL6ATgiBnDNs1NQXQ42nns1Um1Vl/skhKC+fAu4QW5VLdoygzjKk8iY3R/N1TGJCgsddyd+QYvN0UyXt5HrGERMhBvbgMh+iKY6B6LN77jlR/BjUR05+lD+lmRHbSKmDMS3qdW+P7ebXo927B8DUL79C6pTd5EcdGJKDsLhvciygSEbSJKgri6RgwfH0diQQFSUh3PPvZDs7K4dmNPSxpGTOwZJKsPl2snWbae0GrrE6IwPies/vvkjlyuNMaPfZ/2Gsygp+YzMzN912X8PevB/Gf/1JKXmswP4NpTSMKgQMiAYSGX06JaKpCGiCFkR7Pjnx1iWoKihiPUx0SyO8lC7+koAnGYGmhSF1FR2vtETxbfjJM7YKEh6ZxH57yzi0Nlj6fv9HsoSfv4hHzv9QR5YeAN/Dhewp2J7t9czAz4AZIeD3ol2LCQoBXhn2/vNJGWfap/Uo6IyME0L+YhNeico+O4ZrL3fkXHjN/DXJADq9q7EM+WXWE0kRe6CpIgufDqa/Vysf42kcBwpyH2Gj2TTN5spz42kOG8fq+dvQNEszvjtBTi6uNB3hmDg+EiKXlpGXXQ06TdeToxTAUNvmp7SwTAQhj1lQShgf26EqdttIYXr6XNe/3b9rf/hJ1is4XZ07s0SXZnD6YYOn/0W9n4DegCQ2FhzNwWhUWhyCEsoGMLWVJ37KwfqkewcPYzQuk7Lzll4JwXuz8ACR0yL3sNU/ciGm8IHVqCrYYRmFzwUmgBNAqeELgy0Tmrx/Ea/3X4Rgs3XzSAuqvti449fXMP+orrm33bshXOJvRD0ihpKH9+MpDgQhoXUVGizZOWb7Kn7G2D7IQoZcMP0CQtwJbTU5amqOsz8+a9y+LBKZGQ9c+eOZOzYc7s1Jq83kdPmzEMIk4WLTscyS5F0L4NTruSg/zEO732fhsr9IFn4A/mYYT9JqXPwePri8+d1e9970IP/i/ivIil6aSlGeTlKXFyz54IcFcYXtwu/uo9RpfN4LX8Bsn8f2wwPeCMokMfi0w1Wro1BlkwMkcT+rDzGJzeSnfkLzhs6ifToFmOnbw5+w90r7ubjaTL7egmmlMcycHsNnuVbyUl2sz81iqLPn8EbjKTRlDFSjmTvtLKZlyScgToytQrsIrMtlXT6pY7j3F17GN+YC1/9ASSZcFWAYEMEOCOb0kJ13JE+tIQ4e2qgvpYULRNZG8GQlEHsuGIH57x5MfnSfn7cshJFljHjRkLoAMv/PozZjUGqpEEkPLSm02Pp2PwGKUYhxl9t0lPmyCJ1ip3pYImfXz3W3v8jU0H/jkhK91hKbUUZZsiLGYIvHisCbKfU3B27GTx+3HFvOxy2fU+czsjurRAKQWwsvS+9vtvbCN73IWZjx/unmzqg4eyiSvPMxgaGhXXYMc/+QJIhexrhDR76pxQw66GrO11XCocIu9zs2HsAqa6WYRPHt2vTWLETOU5i0sQfcCfa0QQhBKbqo1ytxNHHwF9aByGBYikoQkUVKjIautdkRd4mVj2wnsykBKKiY3A4NDSHg+uzDf6eZx/XI4S4uwgZJs4OyLOWGIs5aC8FSe+T81VVUzFNCSM2DG5Iqz4ZyzIQQsehJzQTFMPQ+f77N9i0qQhZMZg6LZWTZtyH3IVZYWeQJIU5py7k+b88RbQzkjlnXUXhgveo9H5NZeBrAGTDjYRCWeUnAPj9uSQlnkpCwjF0RT3owf9R/FeRlJwZHf8h114u4U8LUXr3b9CmnMw+XWdfUWHz8ghXPNe9YtuZP3PDdwyOGcgF02fRL6kfmtpWwFcVsEPgQafEiIt+xxWjb2xe9tRFZ4KAhs82Mr33tfiEnw9zOycCMWxiNivafW7Heaqg4m0ASpfE4StruRglDKsnalhj83sHcH48FBtvtOyTHEmuvIubt1/Xpu99zgGMNiOJ9m/tdFwAsqKBAT68NHozSLj2q1ZLpVb/t4c41pRSE0mRmqZ7joiTdcMkFNYJhXV03aC6shILCcMEs6IKRdXQhYQuwESitr6RaF1m1cJtLSUJJFpSYCWotAThqFiMqo41MusP7iR95CCiuhkROYJg6AhJ6eZ6oRB0UVG4IwhTYCtQ2qK8vISGPeXo9MKpdWGe5k1kc6TCmBt32uLdpmrK4S3voTm79qjJ1YL89fLfUFbiAzSci9bxTLiW5Q0BNqkuCqNimdBrJkHO5HmHxP6C1QyJzeLboh2E5KEMS0ph8FlzOuzbNE1in91NpNNBY309eWUVUF7VxpdlsprAaiMbp3p8Xi0hw8LRieDVzBbolJFQMtoWrlsmotHCG9mPARc80669ZVk8/LBd2qFfP4u5c39PdHSvdu2OB3tWbqPGaiRJiUeWVabMWYqhB0GyBd+y5ER2qtTkbSBv601klRQQvepcrNl/Q55wbfdLPPegB/9H8F9FUtKefJLyxx7DqLDTSZNuuxX36NFkSjJC0pFfd3FFKMTK99dRGE5h9nVjkSRI7WVPZ4SbDM/21uznFz+8xlT3VJ4+52m7mBx2Jo3adEhlSebGVgQFgDG9CO88zMzx50MxeCUP11/9O9xxEU11UYRd8VfAx0/eTRAnvpt3YwoT+YgQoalvj3ZkCkJg/DiZ6AlpJD/9Hlg6gZcuQw/uQrt5CRg6/sP78Sy8GqWVMPGVi55lf1EeVlPBRNMyiI+MZWBGXwrfvB/X4RWE66pxtIoStUYgbhBV5T7i7z/A0Sbsu8vs6aWcqgCzOlhXUQwm5D6F/tf2J34ApyXxzsHRlO77lEVvfY6MXeVWPkpgYkgKb/W+nKDScbRgYkhlmimzdX7X2gmo7nTJ3zzDuXNVDjOcuWiYZLo9/Hnk6V1mdqxcdQf19d/hdILL1b3sC622BjO242PdKUwJ6aj5rEc/f4rQ+hF4SCUYVUJ2F+OULRNxJNKitJwKdFND07o25Hvn9HOxJJl3lAB/ySvhQEYfrnckQytOtoyZAEzcXAt4gHIgGaQ/c6NjG53d+yuKwk23tnVZtSwTPRQiFAgSCgZ45as1rM4HdwfmcJ1BCEHQMHB0Mg0pVAFhhZG/+rRb/cmyzIlT3keWLYLBbFau2txkmKghSSqS5Gh+liWN5OSpDB9+CYdzc/j6paexTNOO2EgtEdSQL4jL0lG9Ycp35ZA0tB+a0vS33opvRlYFGLujGF11YfQeh7bgLtg5H05/HFKPX+jdgx78b8V/FUmJPvMMos88A2EY5JwyG9/adUSddRZaUlJzm+COA1Q0ZBAphRk4PJu1C75h8au7QNgGaA7TxcTIEIf1CFawgvEftg9zAwzSBrX/UJHJUPqSUNySl1O25CDDLz2xTTMhBA7C6Kh4n7H9FLYkT2H0dd92vGMCZKcDJcGusippDkRQhuShAM1TAlKrUvYRHi9j+ndc1UaOy4LDEK7tnKR0JUhNi7bPpgmejn9e+wa58QZMYmMnt5mOOXLp8AdDVOyU0LKziOo9ElVVkRUFVVVtMzVVpXT7Bsjdyl2DdUKF+eQc2MX5v/o1nqgoVAQqgsVffUmVW+HqG65HNGUbC0FT1WL7/Unb8pkqm9w+YhCGZWEJgd/XyPq6w/zN58Zo0upsD8VRTwRGSGPrqvnEqoKzkmI4L3s6ylGhfZ9vPZblQZZPJyFhYOcHqhWio0bhyJpFwc1fAcKu1IwAIRF/xVDbmE9TkVQFSVNAUxFhE5ouuKGwQe7hOj5bP5gKwigIhM/B7Q++h2mqWKYTVdXxuoNEekzcTpMPVvkQu2X2PD+YhNOGocTFoURGUBc+gy05iRz8zfvIkoQiWTZRlASyZKGoEsUnDWOoBLOnjWP2NPhs2Wr+VKfzsEswdcpEFFlm/v7V3FHVsRI5LKV1+HlnkGUFp9uDs8m1t9pVSMTAtxn97oOtWjX9goT9WiCaCmtaTR42FqRC79o50AF9FqaObB3Df6WDcYGFJEUCOkIYQLDp2X5IkokkhSgr/57aFfso2V8Bwf1YojeypiEs0TRmgSKD5g9S0LCdhWs2cvnQfm22Z1mCzz9dzgW7LwAJtBsPIsfEw8GlsOAueHU69JsFI34Bw85vzgTrQQ/+r+K/iqQcgaSqJN9+G8X33kfOzFmk3H8fxvhZfPX0JqxAAFkSnHOvrfDP27sKSZ7IoKkmsqJSV7cbZ/Ja7oi9hYM+WxwpSzILSxZyyDjUvI0zB1zAA5s/5obBp5HclEKooDA67uQ2Y1G97e8ETQFbowcRHfKRPmE6YudnZNUdYM38+xC+CqzoDBISs7AUB0Jx4rGCWEarsP9RWTFHlklNDqFCCKp25hPdJxXNa99JH6ho5IENeYQswYW1gksA3Qh2ePyEEITDIQyz4xTUhAh7n7yOTgrJJTg5LA3nzJM+63C5VlsMn1/DlJP7M372VR222RQpWJq7lZPGD6I6WE7jrnJGjB2IJ7GFcO768WuCvkaSe3WcUiqEoCb3MFslif69W0c8YhlPb641dYr8Ndz1QxFbD1qkuPx4+oao87goC6n8UBDPnobveGDUWUf1GwAxiJNmPN7hdjtCqP8EVFnCERMAy059F2F76qDmk7IO19mblM1lJ3hhyVakRh3nKtut9bdpcUw/K4bnlm4CIRHlUnFoYap8AcrrTRr8MjUNbvIPJxNhBZAdgurFO7B0QEhok6ehmkEigkXISWlYQsISKqaQMCwJUwdDNohuaDEzO3/6ZM4/anyXDZ9Bdsk2Rif0I6JpusxnGPRdsRNvNy+ee/K3cN2iazGxkJHsh5DxyWEkp8Hk2MuwLOyIUtOUnj2TJwC75ITa5KqsSgoLSl8gy9vY4bYsK4wkju+UmJJ8BqVl8zll1j/RtM4LO5qmzqLvL0KS3iU+G+IyYPIJn+GJaLvOpv0HWHL/LQSdbg71HtB2fIbF755cwQ+1jZzlVHBIJttfv4yRt36D1GcGXLsStn8Mm96Cz68m9MOD/JA+gH7T7mFQ2sTj2q8e9OB/C/4rSQpA1Omn4506laJbb6Xq9TcorUknqCugRjDmhCgi0m3vAVl2ojj8zLjAFoUeOPATBYf/jlzzHBeNeZ/ExAF8+OFDxJbGciihhaQ8vtNOD/5ix8MsuGApv/rxHi6tz+KzgamcXKaT6bcjCL1ObJ+ZocoSUb36E87dx6STr2dVQwmpW54ndesLHe7Lnpo0Gvx+Wu5NLVorQqoP1xANLHt/PxGflVBVK1EtOVHMIs6+82zSB2by3b5ylrht0iGHJS4BYt89kXxvJuVT76f/iuuJ9BlYQkLFor8kKCaJo7Emr4BP164HPDyzsYQ3CgJNGaISh+uDEDS5v4+Tem1yp9+N1Dz2LjKAmqYxhNWqurPZXmhrdTFHf6T3CUrHAl1F0XhjSx2r1xeTlBFFVVWAytUmP94ynexoN+nLtiM6KCMgSUFU9fg0LKGoOIjxknVLC+GxwgZ1X61EcrvsuECTYZwwTDAtvgy3bENEtEQA3iiuJrIqjZxAJrWH6nnqyjGcPyiVo7HnfbuA4GFPGievXkxFwR62v/UM/Rs+Qg6H0cf0QdbqkFUN2eFAUR1ImobidCIZDSQ4Al3ukyRJTE0b1eaz5krN3az7s+vQFqpcfk60huGUHZjCxBQmhjDJ9mZxz1m3dKufI8h7+zui1Y51Oj+HpERHj6G0bD7VNatJTjqt03aKojHn1E9paChl1/rnCCifoqrtidrGrZuREHw/7WwiDZm7vt+NKoEqSRzYUsaqWh9PT+pD5Zj15Lzze6b5VlGUt5v0vsPsKbvRvyI86Azmf34xrwYPUR46iPzD1bw155+MSe7Y/6gHPfjfjP9akgKgREYSc/4FFN10E+q7T8Awu5jZ5jX1bD2wFkbFYuRMxXQEeWrnt1wzYDrZ2dPYuy8Vj6eEt956k2AwElDpS18yGzPJj8wnP6qaalcQxbS1L6d9OgOA+0+yRaovDXDy120BTi01UDrwqQDaaB5OmPsQhZN+zV+W7GFFnky4LowTHQc618ztw+TPrqbY2ZcRR1ZoNYUSDgVRGnPsPvUg5YFYLCFjhH4ibBziowe+54a3PsNsWuWn/tmsKPDyvfM5Rux4hCzfIXovvAYFi/J4BwXJtyEUld07dhDAQ4uBvI1vVq1GPXSAaGUIuWUyoqwcjjjfCpCcCvSRqO0iN1g0RYK6SoE+QkyEsJqnPIR1VGTnGIkfsiQhWxYJXYwlM9ZO4Z05MJErRvVizrMrOP/t9bz6i9FIVUG2bz7I9pwrGTjzHpwxA5rGFkJRj88JVLFkcLTdX9mhEnvBjE7XqflyKyfqCh/NHIqmyASnjmDQfQsAeGbezuZ2O8obOb9p9nHpwR0szd+CJmuknXkyyRt2kV1WwtbPXqV8wVdkrs4j4ADVBGXxbuRODk34sVeoCXSu5ekMviNOuI7uTasEdJsIPTj3cVITeh/39o6GLgxUpePTnmWFkMTxTffExU0BoLZ2Q5ckBey/6ejoNDTVS0A0ic9boaa+gaId23BrTnonD+SQJFhmhjAAn2Xiq/ZxRmo0551jGyQGznoAPj+Nxjpbc+WvK+TzlX/hrbJVVMgwx9OLa6b+mbu3PM0dy+/g0sGXckrWKaRHpB89tB704H8t/qtJCkDk7FNI+cufca9cRz8OMeTq09laH2L3J7loS0vRTIkDyVF8VhFJtmcL08IqHk8JAB5PLcFgJP36WVxwwR0sKS9n/dLNjOyl8NLJ57K4dC+vbH8LRXYwq/dU7mtlNvt6XwdDq+rJeWkRMx66oN24Wl+uZFlB9qSxaOse7CiJioFMMOIQTy3fzzDVRXWinRIZ3vANcrAUAax5+VpOKPuw2YpuxlkRHHKYCGGw5M2WqI/mUJvvcAekRjKkVzTQH864koWLH2XOCtsnoirOweCzrsfriuS78g/wlR+muLKWtISY5r6EaWLGxrDtpqOD/y345ieJ2NjOU2OtJn+UNlNYR8EI223yt+4lUGIb5IlWviqWbiD8wWYC83Nx1bB0XkjP5fMNhfxt5iBuP3coT3y+k4ufX4UT2EQ2pbWfM3D/CQR/8z3OxFEoio56nCRFtWToZHqsMyyOAjD5Yn8uvxjcH5cqs+H+Wdzz4z4yYj1MyIrld39fy77cUh4u2sMfzjqJG1e0Kog3HCY4LG77HNz3P0cmUNTLzawfNzc3MQwdQw8SDgXQQwEsPYweClKX5yOc2j46cyz4gjbpcB3DY+UIQk1Tjh7X8UWmOoMhDLROLPUtET5ukuLxZAISjQ17ur1OQ1U5xNkpx0dQGgjx+H13kl5WQNQFl/P+3LZat+/yKrh+RRknntRiDBcK24Sv8dtbuW+ZyvLoeuplmTOcSfx26kNkZ9geSM/FPMfjGx7n+S3P89qO13jv9PfoE92HHvTg/wL+60mKJEnEXnghsRdeSE2djyu27CHfF8Q9W+PTkX2QEYzwVbJ4dyVlm5axTpmHI0IiMqjTv/86VG0ZQd8AVu7dikuV2ReZgh4I8uTWQ4xLSGLe7Meat9Vr0XquHG6fnPMjFM45OZmUOpX1uo5Ds0+OBeW1VNT5qK8PEzAdLDtczZjkKG76ZGubccdnzSfsXg9AQsDAvWwhBVvOI+PbX4HDDqacUPZhm3UKXnoO18GmysIjW6q7+usa0MM6KO2v6fmpoxk38WPeTSmmsOqv7Fn/CudNux1V1fCIAK+9+CwTTz2P006w4zjCMpuzkH4uAnV2+m7JvsPQyc1pZZG9H5XLt5MZMZipyRnsfvzHFkUs4JcjwNn1lIQlSfxYHyBr2248qopXU4lwaE3PDraW+6kr95OQahuG3TAmgzP6JvDp3jJiQrU8/F0x6vhfIe/YQuOye+Es+5g7HcdHUjShYDh/nsjxD6U+7j60lmGyybyTJpAeF+Crhnpe2l2HC1izr541KHx9YB40+Y8Ni0pCQcWYqPLyaDglYw4jk2cwOTGjTd+qqqGqGi53i9+LEALr0Gbcx1EF+Qj8RyIpWvfIwB6/HQX0OLtv2NYVDGGgKZ2QFEtH/hmnRFWNJBAs6Hb7+spyImLaRksLgyFi62yyffWFv2i3zpfrDgPwxKK9fLG3FEWWcJhufuE8nUXevayMCnGSNpBbT/kLvZLaCuLTItJ49qRnqfBXcNWiqzj/q/M5s8+Z/Hror+kT00NWevC/G//1JKU1frrmIs5o9f67Vq+vBvzAXvoiKxY3D1gF1LK7dzSnH7gB1tQBdgQkD3iRnQgJVt8zk/RIO2qwOLb9xbs0Or55ViIQ0jnp6eWYKIAbGMCCl9aQ3ieGv84ayJUHWlJpg/LBNjWYvY11+C+7EJqCMkdmSl4xzuRa9RsAHD6Fw6ka/Z57nlkNtfz4/CsAvHb9pewZNBZmnNtuikWSJQpdyfQZfhLrFq3AFfoSuJ3fnn8qC9akkL9hMasWzmfVwvnIWDgl0BPbVnM9Xji99l1zXEbn1YmGTBnN7qVQ4NtNgW93F5117RabEvKT447iruqjKyMDhoVzVTkOt8J7vxzb/HFWtIfbJmaTl7OXhwFvfDYhtxs56MPvsy80Llf7rKj9FVXcs3kPOvYfnirZBquqgIeFE2OfyQ+f7m5aIKOoMpJkkOwoZfCEsUietsTn9mWLSE7LZGlWOtsklXWal4yVuwAnNKXmhkfE8lCMScj3CCuLJlHeeAqRMdsQZgM+yyRoWWwP6VC5gLkTb2w35o7QoIdAUphXn0LhFwtwSeCQJFwSuGQZpyzhVmRcioJLUXCrMh5Vw60q7C0tg4g0irdvIpcwDiFwOd1IioKiqUiKncElaxqSopLrzwdAQm7xt/mZsCwLnc5JCpYJP8OEzelMwd80zu4gObsPPrGJb158z56SFAIBeJpOx4vX72XmhLbZgVP6xLNoawl19WE27a6wM9RCJo2jbqUq9DyRwuKZX73X5XYTPYl8MvcTPtn3Ce/seof5OfM5q+9Z3Dz2ZhLcPfV/evC/Ez0kpTUkGYRF5rW3IkkSsiwjS3Zl2dDhzRz6YiEAlimjy33RrFyGHK7Dgc5pQ+r5zbRx+CwVH7HM21PKohUFXPXFNtwO+45T9TcCMW02ebOrDKc2CoBgWMdEYU4vi1NGZ1OpOnh+ZSFFB2u58rV13HHxcB7/aAcAsrOlBtADlyqMqZ3IuKyxiIMvMyiiAEmGHCuVf6TUkeyLp8SpM/DkBxgSSKSkwWLYuJNZoPwdzZQZdfpvWVuaax+Co0lK08STJQQpyaei1S+nqOoQ6fGZXHnGVP5hmtTW1VMZtthwsIZwjIvH5077l74Grakonsvb+ZRQ5vBsrn/jQwKNARwuFUmSmio+Y2tgLMHnrzxLZe6+Lre1afYkqgJBGkJhyup9PPjRevLrVVIiTMKmRUXIwceXj2VgTPs7+aDfzhJxutwI1YFSdYjaWvuO2u1uT1Je2p3DSkcEWYF6DGRMyTadMyQJMFEtSNxRjWaBwxI4TGj65VC0cB31GJCdTtyYZBJHJxIWBimuSN6YPZWQrjNs8UbclskVSdFcPrgviV4PBfWNJGoWq9fsZ1LW6Ywf/2C7cZ3x0Vi0TnQaADtztlK6YI/9W5DBL1kweAiKZVAmqYQlCV2S0WWl+WHIKsYRMnAkGxcgwpZ3l61dwfyFX3T53QAkZvjJHQajPxiNJJoKTwgFVSioKChCwcAiLOkIyfYZavOv1WcAKGAEjQ63ZRJsMwXTXXi9/fD59mMYAVS18zIERxAOqwinxP5Vn7cbgaT2Yv0Hh9h1yGTOlN4MSLWJ6WUTMrlsQmab1n3+vMiudn4c5M2turli6BVcMugS5ufM54UtL7C8cDkvnPwCo5JGdauPHvTgP4kektIKE84+nz0rlnLBSe1tpnY9/SqHgD6JgrOf/hzZ4eTwyxcRV7oUtyp47vLLAaiu8HHwmU3cbMHviYDdLWm879W/R4ljJrlZtvBtZu0Csgp0fiyoQlYU6kMmM4p3kzxgKuefaIdsM5Ni+d1r65EEPDZvZ7NWxZf3e5yJi1AjDrC3t0T0kL5cU/QZ8VEtYed+cgnnRYzntezr2aFk8nZFJOyCuG905HEy7skDCa/cz+iLplG4umMx3RFrewuLjKT+lNRDQXk+6fH2CfOqs2YAsLa4lg+eX0VmbCzjM44tzJO6k7lzDEt7d2Qk7sjObecVp4NjqWcVRSEpwktShJcn5q0jp87BjFTByhIFHQd/mBDDmD4de3rUNjSlsjq8cPJ9eD67FfHZJST1diAlttLHWBYhfwNBXx0xSiRrT++AxDWZr7b2Kz1UlseJO6p4oj6HcfstghXpxB+sxcirI//TvYTcOisPbqB/zmgy+6VxYM4J7brNiIqgrr6kaV87voCGhYlT7lwjkrN+G6OKMimMqkASEGk62LiogbKJPsae27FrLNjfX8AwaAyF8AVD+MJh/OEw/opyEn95KYeWLmHP7m3MmnEqzshohGliWSaWYWBZtuPr8PV7GZUXTfyJAzAsA90yMC0DwzIwhP28q2Af9Zafvn36okgKsiSjyE3PkoIiKdTU7ENVNpGVczGTGkdy8JEmw7ZW1/aaiT8B8O43p2HhQKA1PTvQhYZf1YiKj0aRnfZDcaHKTpz1fiKBFSsfIiZmcPMyRXWiqk5UxYGiOFFUB5rqQnFpIDRu+fALu3RDK7y9KIfyH4sQS8pYsKSMT/pHMGx0EjPGp6EpMqoioclSS8kHCWrZjszxaWkcioNfDPwFp2Sewh+X/JHffv9b7p14L2f2PbNTzU4PevD/B3pISiskZGTRUFWBr7YGb0xbE6oIvQRwcLBCQvfV4HSkkFS6GKekY1gtEy/bXtlKf0siP0bFn9j2otB3hUTSwve59eNvOLBzO1+9sopCoJD1zW2GA2NfXsCLK5Yx9r5bGJMYyQtXjeOF1XkU1QZorA2BIkFjL4LGJUQ5vsFrNPCL4afjGjKB4CeXsSVqJifU2rU+br70TU7YX81FZaX0PimLvMLvya5LpbysmLTUPhSIA1imyRQrhzxlMNevfB6X6iHSlYpDhj2+MJCAEIKYiDRKAF+gtN2xM5vIwC/HdicDo+u7viN3hd0su9NVRx12YlkWC99fS+G+OrCgzmmxQrbY7pMZEWvy1k1nc6CwnFpfiPEDO98fEbb1LrHR0URm/JYGZxTGN7cyfG89Yu8l+D9zI2OhYODG5CUUPko+Dcua2KVj7RG8uG0jkSRy5pzTyQ/9xJp8k989OYnyzdUEt1dAUzDtrfdeQxVuoiNTiRjchzlTR5Ia1ULe9LDtAKyqHUemwpbAqXSclrt7xxZS90VS7alnyj12VebqfQX43zpEYmT7FPTWkCQJj6bh0TSIaCV8zWoSee/azR5gwAWX4E7seLqhpHYegysj6X1K52Ton4++iqRIXHHWNZ22+emnWxA0kLk3GzXGQCQdsf23DfNAEFE2mvr43QglFUmEkQmjEkCmDkkP4tJ1PH4JRYTtB2E0dIINXgy3hqp+SnV3Ep4UsEIR7QgKwBWn9oNT+1HbGOIf8/ch7a6l6KODvP/RweY2cYrE1EiVn/BgbPUjSU8DcPDun5CQkIV9C6CiYBlBatc8ju4rQtjliBCShJDAanp9tSQwsbBevIfvXfcw+dl3iR11/PWqetCD/wn0kJRWyBw+ClVzsP7LTznpirbF1RJ/9Txstq26y5+ZQ1pqFDRYlB2M5DeBz/nytytACMYmngPAyOvHEBnV9sT/Q8EQdu+v4pt7/ookyUSm9kf2NIWXJSAcpq7gIEKW6L1nA5e8vg5Jgq++/xP3Z/anLqM/CAurrJTXokZQ781kxU1vt9lG3a17WbVkAdesPBuR5aLPmq/YaPQBZCQhiAvY5lFXLvo13sog00lkzeefkx29hIwIN6vC/THCKoZfRUfDRCVJqsShDCUxKolNhofy0jXARW22KzcRi/fXHSK31sfvx2aSEXXs0HdHaJNe3E1YlsW+H7biNwyGzxqFw+noNAT+w7z15K0KIkdZyKrEjlqF7U44M1vhT5fMAKB/r64vwAD1/gDgJDLSPqaR/X+B+OOFlO5eTvDASkR9KZKqIWtuZE8s5eV7ufTQPA4e3kafzNFd9l1UVcxHShZ3Knl4PFGYhoWMjup2kDatF2nTejGU0dTXNrJlzS7278+hpLKQqg0HeXnDj/ij44jqncGogQMYHmOTKVXtWJ9jCKsdSamtqWLTomUM3JpIFIns7d1Sy8rw2dFB1X18tYaOhh6w+3FEdp65IwwLROdZXmBH+dQO/Gpaw+sdSKMPDoz7hNNO69hEcMP3i6jz9+Xqc/7Rbtm5H2xg68Ea8u6b3W6ZaZo89MjDRA4ZwNUnT8YwguhGANMIYhhhDCOEaYYxzTC5u/PIK95DkqNjp+ojiIlwcsulIxBCsGxPBcVFDZiWXcMqalctVAbZODQatwW6qEFxVOJ1qvb0lmSn/LurLbJyUgiMHU1lch/bR0hYYAn72bQQwrKrOwuLGn8VA3fWUvH7m3G99CLukT32+j34/x89JKUVPFHRnHjxZSx7900kSWLcmecSEWe7lX79V5ugpLvr6K0WIioEskuQPKqBk77bRk19HCDB9HMAqF5XSuQpbeeQEx0ZRGsJFBbkEbCOxB5MTDmIrEqg60QEw0QEw6SE7IuCEPDEL0/h5jWfoiw/SJy/FoAZgwRLEl28u+xrbFtugYTF4j1l/JRnZ2hYws1mPQVLkokXFUjlguiwhwJnOZMSprDab2ts9n/zNZIsuCLmRS75658pK/+G4uJ5dr8o9Op1BY11XgQmQYaSJP9IUeEhZEXFZQXQQ3489X4GSgXIhwTL8iMpqw/x9tk/7yTXQlK6F0oxDYP5tz9ASeN4JCWaTYu+4TdPn22TlA66KN5fi3AIrnvclklvfWwFcXU+Xvxd53frHaHBH8QmKS1RC0mSSBk6HYZOb9f+3a9eZMyhecREdOyA2xovbV1DhJnIlRPt+jembqJIRpsaOwBRMRFMP20i00+byMMPryasBOk1SSPv4EH8+3azeedWDsUXMGQoHCx+Gq83ld6920ZywhY4jzI4y399HQOrbeHy7j6FzLq6hZQaQVtgrPyLJMUIBZGEQHF1rj3CFMcmKZawa1t1gbq6gygqxMRM6rwfEULQ8T4FwiaK2jHpVRSFQFQ8ep1ObGxmh22OwKMWsnnXG9SZlbx8w+dNRS+bylZIAMJ+blUQU1EFZ944jaRMu+zF/pq9UBnkvEuGI3dRYDF44DCVOfkMmD6XiedP7XJcR6AXFVF4yy0cuvwK+nw5H0dWVrfW60EP/qfQQ1KOwtgzzsEyTdZ8+iFbFn5N/4knkpUeQWFTGPek6+6CiWcgAfrnz6Jtf5DB912F66w/YJoWe+9aSbQiUbg4j0/2l6ENiibkVpgQ7SU6IoNZaZfxZa3e7lQ47aoMtEiTiiuvZMWgCazyDG5elhhTiTh/Eide8jZ7/vk2PPoooYHxeCSd+xfI0CbPJ4MIzc9LF/dmYHo/UmISyP9hHrnKXShfP4lBLMOuOYnJ6efjD92L/9JG9m/azPbFrxOVXE5U1Bji4iYzoP+DlJV/y/79D3H48D84fNi+u4xTwRUwSX9jRJvxJwGLWl3nHql/DuiYpNjlc/51TcoR7PtqCfnF23HFziQm1qS2JoYvn/6WQLUMUhKN9X4iolqiCH1GJbHz21p+/HQ9sy6YgF83cSnHnzbdEAjjIozWgXNoR3DU2XqhnJ2LGDDuQmK8MR22K6+r5H1680f5ABERtn7FNJpISlcwBJ6ICK49aQKcNBXTslh98BCb960AluGWS8jJvZTde7zAYOLjptK//1x0IXAeVaQxodomXqWuKjJPGt6GBJj+MBKgejuvsNwdGKEQ8jG+Y7vSc9cRNUtYKMcgKZblRxheTjjh9i42FkbQ8T6FdAulC0LgiI0jXFnR5RgAsgf2IskdSVW9j6g40Sx8PRI0tCzsbJ+mw2IaEGiMJ2/rwWaSIgwLIUSXBMVeuanT4/AK0tLTyXzrLQ7OPYvD115Hr5dexNm377FX7EEP/ofQQ1KOgiRJTDj7AkbMmsPu5T+x6Zv57FtdjlcJM27KGJLG28Yd4ZoqfHEDsMx4pLJ8AEx/iKUNBgkxBnejYx32wWH7xPXc8FhucCpcAcyMVHkzxiTrsH0mShom03dIOjvqSrjw0ZftgViCKeZSrpZfRpVN6hvti6yQ7XwbtyPM2YPKeOu8y7CEfeslhIRAwqWpuB0tX60v0YJqqHA0EjM+k6h0O/PE4/TgcXpImD2HkOMhLKuaZcvtooS6Ppw5p84nOelMSko+xTT9SJKM94s7cAdNdk1/FSyDkNCQ3dE4VBWHplK1dz4T97/FmSNaTKeO+zs4cpdvdW+6p6a4CEVyAxIjJmazdeM2Sg7GAhNxeSfyz3uX8etHZuCNtKefpp05mgPrF7J3ic6sC8BvWLh/BklpDIWJlDuub9QRrj//Hta/n8uEJbdhLLmT1QMvYfIlL7Vr9/fNK3FYiVw1fmbzZ82RlC4gGRay1rIfiiwztV82U/tl85vF/digh/mgVxklpT+hh7fR0LiRjZufwcTDj/nbiFbeZ+7wuUS5o4i8cxh7Xl5Cr/pEeLOG+ntqiYqKAcAIhdEAzf0vkpRwCOVYPLQ7JAWrqdBf5xDCD+IY4xVhkDoWYod0E7ULUhCfkEBNfi6mZaEcQ28kyzqKEuZXf+7c8PAIyvNLmffo7jYRQWFYxzgiTe2aamt1pH3pcnweDxn/eJPDN9xA3vkXEDVnDsn33I0SdXzePz3owb8DPSSlE7i8EYw57SyGT53GznsmMSiqAveVH0HTCeiHJ+aTX50NvEFWwxK0L5ZSU5JHo5RBsHwx9808lUUbdKbX+0nJncenKRdy/sa9BPa8Q+oJAfqnT+f7hDGMHrSGkdkj2bO/kNryfK7a0Z/oyHyKlAzOKcuhNj6e6IHlFOcl8cJtD5Jd6yQicQyysNAUlUh3N5w4my5cGVeNJSVlSIdNhg55nAM5H1JdtRuPtxBNs1OdFcVBr14tLqVW3W3IlkXy0vuRhImMiSQsZExkLPopAfZEDmbEyPbTHS3oKrcHdu6+CYCNX6zE6YkhNrkPsl8jZcAYXLHtLyKJ2dmYKxU0wBPt5LKHz6e6tJrG6kYWvbebULUDf2MQb6SbUCDMks+3EKppidYETAuv82dEUkIWkbJ+7IZNcHtimHD1ZxQX7+Hw4qeYvO89SstvJyUpq7lNpa+et80UrhX7iY5pqY5tGhaK1PW0h2QIFEfHF+uAJZCExrBhFzFsmD11U1V1kJzcr5nj/Za19Y08uudRXl7/NCfuS8IpO5AUmaSGGIY7xtLrLwF8SbFIsoRcFQBU8j5YheJ1IKkKssOu0ixrCrJTRXIqNBgKSrQDh8uBw6mhuhw43E6cbicOlwM9FOJYMShhNZWt7gKmODYxsIs+dk1SJEJIcsdtwscgKRnJyTRYJvvKKxmS0rWeyTDNZg1Xt9GquTBFt0hK+HARAKEDB4ATu258FByZmWR99DE1771H1RtvgCKT9te/HlcfPejBvwM9JOUY0CJiGH3PPHhlChRtgv6nABDSVQQCCYlXpMnsXetDFUkka8U87xqLe5tMo25QnrGN8owB3PrOXxD+AO8MnMW7EXO4XX2TcwZ/yl6HwQsHi/llShFS9WDScycDiQwEtgdSCeVmsU5PJdKfwoTG6RxSgaGTCCsLqK/f0TzOal8hz6+9ne01h3BItqFWpCOCK4bfgJtj18JJSppGUtI0hBB8/P6ZJKTsZ8t31wAKUvN0kkxqfBTJFTW4DIWKUBJqShQoKkLWsGSNVzzJaJ7+DO50S52jqHgehw//A59vP1GZvfCVelj6+lct34XsZPrpVzDysrZVhzNnjEN+3/YxcUXZE2lxKXEEQybhagfRWSaaQ+GnLzaye2kZhFyocWGmnDcISZIImBaJ6vGnXTaGLCKV7pOUI0hLG8yhqDQaFA+xMcmYlsXaVe/i3fkx69VU5KzfcPWotgUYTcNCkbsmKbJhoWptL6SWEJy2YDn7VDdJR5nVxcf3IT7+JiZOuAkhBJsObuKrxx/GHTCRMqKwTJMaxU+SlQGyjNWoIyyBZMkYVhhvtRe5WkHGTveVW3mMFId1NviDgK/T8YYbTLzHyPTCBKRjT/e01thYlsV3rzxAXUMjuiVhCJnsSesAWLtgIBK2bUuRJLAUB0cYQLQWoMyI6XAbum7hcHWuwRmSmsQuYOlXX9DQrx+KpqJpGoqqoWkqqqahag5UVSWs6yjHiG5YRthOPOoommhaiGMdN0CJjgbqUJOTj9m2w/UjvCRc+zssXyO18+f/y2Z6PejBz0EPSekONv4DnNGQ3uI6alkSazO/ZFvaElLCs7hm+3Q8ehSbHMm4NCcNUiMjvRZLmtoX9M5g0L597InNYJyUy+BBWxGyRCwutpSp1ET8ggnmIA43tY9X87k4+wm+VpL4MNJLmtX2ntPSVBxO29nVF6rl8m/OpiwUZnR0HCaCkCXYU1fM75bexe3Zc0iiRZDaFSzLImRJSLJFtWsx7sAA7HC7fTdbGxVHRK1MIHw9htyf7NtafOtX5B3iyfwaXo0wsEyLmoCOU5OJcLYlAAKpXebOnj33UlzyESATGTmMy/78Kg5HIjXluzm0agHyigxyzC0s/uYNArX19Jo8itheqRi6Qdn2vciyPZXjimm5E46Jj0BoIerznbx/70YA1EiFk3/TnwEjWuzfA8LC20kEoivU6xChdj8DqQ2cEUSafta/cwURjYWcWLuLvKj+XFO/ivLITOKSHmjT3DIE8jFJikA6iqRUh3W2uaMZGmrk6qzOnYAlSSKwr5ToWoMJv/w1U89umYrIvf17wtkmfa7tvHI1gGVamMEwhj9I4byDsL2W2b/tjRE2+P7lv2LoYSQlFk/MIPRQKn08iYz8ZddF+RBSt0hK60hKQ0MDG8tVejskoj0ONBmMqv44Yg8RK6cBEhXhOnZb9WR6E3Bo9m9nQ81hVtWWse6zh9BkDVVW0WSVKsNJoy+b5LjOBb4DkxKRDJ3y4hIWFJd0vU9AjNr5qbc+ZwOed2ajyhbJwPXJEtZmGXOzhEBhIDK4JKyHFEBGSBK27Z8MkozA1qk5TROXFGLRdwPxf/kSEgKfloClphCtjW2WqhyJa9qTxaLdexrduGJPoXd5Fe7kHmfaHvxn0UNSuoOwD8KNsPZliMuGYB1C11BN+4JY6viR98csw2wK/Xv2/B0PUSRJoBphqht3M0LeyZtD5tA7rYpHnS9zbW4yu7U4fK45VKacxSkvXUtZwVoCY39LslXLZM/HAJxplnOPnEFxdA6mZKAcKSWvSGiarS35cNvfKAiG+MfJjzKu95kALNu4gUC4gefXv8IPhxQmRJzF/p+2YlYdAlkw8zcDGTwmq92u7nxpKYdqR+KoHMwpZ55HwoAWgzA9aLBncwmHavcQ1eAm6Klss+7SvAKQIvlnscRNNdsIavZZsG+dxSXR0Vw1OYOwKXAQJKX67yz6LAeH00t8RoiKyu9wOdOZNOlHFKXljjU+ZTih+CIsl5dB95zKNw89waqVH8HKj9psO8ozgTDgiW0hKZ4IN5f/eSrrfrRt8weO6U1G3/ZF8YKWwOs4/j+FHF8DdarJ7e9NxSU7caku+2EYuNwxuFQ3LtWDS/PgckTg0ry4HJG4HBHE9R3O0tJZJFRsJeCIYctJfyKrLgex+QC3DBvVbluGLlDkri/WaliwfH8F27/bwXOzBuFVFWr8dvrx5ckxXDy4X6fr6rrO+s8/Qo2OZcrcc5s/L1u4C6fixnCEjnk8ZEVG9rrQvC4kVUOVoP+4/oSDAabGnYbliWSDvpm6mo2ojXW4B51N1hlzu+xTWNBpKeYm1FiNRNbXtOxLUxHDWROGkDnr6g7X8W35B59tf4avRv+F7Aw782XAk7egRW+mpPYnhGQAJkLSkWQdi9/TJ7FzzxxVVVFK8iE5jV9dcRVG2MAwwpiGga7rmLqObhiYhoFh6AwYPqLTvoLF+4iSLQ6nnocUmYS/TsITGQeWhWWaqHUBPLUBnL29tpW/sAALyTJt8i9MJARW4y4SpI0kxznwk4IlBIH6AHp4F0FHKn3iEm2Brmh2529+cOQ9MronnmJ9EAf3hxj684IyPejBz0YPSekOzngKNA+sfw2CtSDJ+I1XSGloEYearbQJKyhiKunoQpAXlUpa4gYOZWXwTulpPCa/xoo98YwreYWRvu8xwxuBjRiuvsjGIRqjD9AIbNb6cdOMs3ikZDn48pjlH8u4xAa2KIUsd+1imByJsBxYlskPBcvo5/EyrveZNAQbeP3Vr3DusS/GZ3AtEhChQLwqU+YJ4vM5KMot75CkJE7ORvnaxb56jYl1sYSL69m3qhAjt45etTpxtmyXgNHI8IdaLmZCCOb5TIiAnVIDc3xRjEyOpd4w+dbRwMM08PiynZgy3CEPZoCRi5B3YGjVlFeE0LQYRo3+ZxuCcgRWIIyQnDgjY7jg6UfwlVZTtS+furIyVFUjKi2JsgMqa1aV4jjKmyYqNoJTLpzQ5ddbLVl8VViF8Y83WpVCsKcwJARlpkaVlIKieOzUUMl24t0n9QdvPV+4RjKpYTXBoEkQk6CwCDZCUJIJdRXWl4AkJxCA/DcBuC0+iSuGndWu6Z5CO7X1r3ctQ1IkUGzXUVmRkVUJWZFwWAKrXuen5QUMX25nEv3hggEAxDi7Thde/NnH0FDLtOtubhEuA/4DVWhImPrxTWvpIROladdV2UGSO4M3nIvBCZKjN1HBSHr36sYVT0jdqldZFaprfh322681V+d1m4TVJEJuNUW1/7an27X74tBGHlj6a567aiJn9e6cWAAYfQZgVVWQPfDnTHa2GlvIJlnRM39PVL+xx2jdOWo3f4fnq0sYfvnVJE20f1NCCF697jZ8NUtJu/R+hp/Uvf7/ccdK/PXhYzfsQQ/+zeghKd2BMxLmPgtnPA3CxNLDNP5xHb3q4rkl7T5ey3+JRkfLnVzMyP5srw2zd/kriPwayiMc1EixnB5RjeWF9K1O9s0AYTU0rzNm30F2p7uI3LMRAZSnNfL+Lp3t5IET/nj4CgAySEJ3VROWfQhhcvZrU6nVw9x28pWE9BDPP/45USVpmCPKmXrSCKKjInG8kA+AJYfo763kB1njhNM69otIG9eHqxN+y9//+Sorl+QwrbqcPsAhh0R+hhdnbzfWdwsot6qx1qax7YflyIrG4Z0/0GfMVGISUpi5ax2KrjP3+jvoNSCLO4H1h2r4MqccDYmT+/2D5Y9sBeDs23qRkpWGqnYuALZCOkINNc+He1Pi8Ka0rY1TuH0HqkSXaaKdQZZMLKGwp1yz00EFWMJEYCCERJmIwhABVClkp04fueOUnBiJGQTjR/HMmY+S6Gq/D5ZlEgrVEwzWEAzWEgjV2e9DDYTMEJYZ5qe87/i4qUDdzDNf73CMqbEbKasZgSR5EIawzU0sgWnZPiGSBbomcdkZ/RntEqw5VM3eHRU8s+wQjEkg2tW5aDQYCLBz4de4k1IZNf3kttu9dDSVj21Fiz0+Yz4jbDbrLmSHgjkuGXbay1KihmNEpuKIPEZKNYCQ2033pPy0Bdkw+fLuO9k9+AriY0YT17vlAhr21wPgcHdeMiFk2e2dWtf7FTDs7C1vJ269rRGV2ovAvp1YltUtR+HOIHR7m0oHv6du92FZlBfnEQOIVvsoSRK/fPgh/vHH61j6zusMnT66W2N1eVQaq7ufydaDHvy70ENSjgeyPdcrKxrjTklm4w9lRG/rx8uj3sPhVsnZVcThLXUUe/0E6msQVhVObzwJGXEYFYeZoxcxQV7Gj/ED6bPjbgxFpjFqIBMH/4oFCUXovgoILEMCUmpMPnJuBeDNvg9QKfai70tmr1KEXKshnIKyQ/u4PnQmw2OnsmdrIc/P/4zIklQGXuRl9gxb4CssQRH5ANT2WkrFoA8ZAKxacz+6HouqDmXSxD8RHd0SynY47Auauz6EioTx6yFMGxhPQ6Ofl/70MFr1dpxDZ/DNM/eD5AChAyaTt6/C32cowhuNAVitTn4TMmOZkNlSamBPv0oqchJIyeiD2sH8vGmZ7Ku1x234dCLVru/kQwED9ThTLQF0I8zrp9xMjHoXY6d1PDUwedl3DHf5eXXiee2W3bntR96uBq/acaRClhXc7ljc7th2y0wjzG0fnMSPop5xkpc/n/Q0vXp3rPvIil1Kv5gfGHHnD8fcp/MBpsCE+z6EOokRDTA2sV+nF88FH7yDFPAx68Zb2wkjTd0mErJ27FPF9h82sGztCk6dOZugP9zm+8i8YAAPXfAQAIXrcvjyrQIcEccmPgIZudU017Z6Hxnr99GroojDvU6mMaIXsgG1efDStT/hTtGJ72VfTLUuSIpPt+sueR3RXW7fb9rTXB712OnWaRkZFIUCFFZVkZHYeQXvY0HyldnPjmMTo9YIhsLs3fAjvu1fk1GxhAGihDrhITcUTeuYVVRCJKNPu4SNX73MgXU7GXhC1xEigMzhCexbW8KMHvFsD/7D6CEpPxMTzx9K2pBUNnyTx7qvDmKELSLjXZx4/gCGTU9n49efs+pjGHvGXLJGDid/6xaMJYXIkossl2Bx3wdBUlg2zMPB9c8RoYMp7BNiv7JC1KwU+kkn4nJHMGHKhWwtmce3zs32xlXwBavwep0M99jz6dYOJ866CNLPFcye0XKhk2QJ8+TeKD8dxnDZ0Z5o770EjQJ8jbkIsZY1a89j+rRFzZV7926ys4Yyx6RRv17n8Lc5ZA2M59UnnkUu2Il3zq/JCoXZtcvi8sdeIiEjiR/feIf1O9Y2b/fMky8go1/rcnktMM0Q1YUakhqksmo7mtODqrhQVBey6sKpunlhz2IerbAL+91Sl8U0JUBXzit60EBTfgZJCTTVtdE6v2sNCQVXJzebfsMEYeDphKR0hR82v8SPop4n+/yC2VPu6/LkL1sGootKxR3hj6s+YnTJfvgEjpjaW5KEJcsIWcaSZSxFoTQzkajkFAaObT8tJgt7x6V1AXbs+oJv06MpTogks66cwaEQUlOxO92vs6lsFwAffP8p8WUnIAuN/asOEhsZieJUUJwKqkvFX+5DAhzdKpsgQ6vvddObAxkb7SJKEXi3ndqudaBU43CpAimgdkFSGgO1AHgjup5yqm+Kari7EUkZkNWHImBn7sF/iaRE5i8FwPP3FvIgUDHlFCwlDu2OH5E0mzRVFOVyaPXnSHnL6OvbwiipkSpiyIubymrvOB44kMmbjvbHQQ/a+/XNs/ew5rOxXPnkn7ocU0KvCLY26OghE4er57LRg/8cen5t/wJ6D46j92D7wm6aFkorQ7C+48az6uO3Wf3JP1n9CagOJ0Ozp1Ec+hiKrmV66X2EHZG8Ovti9s7aT0KNg6TACEYXzySqZBF+dx2/vPxRANZ89CCHpOVkhicz4LQZ/LB4Feuz86h2VWNWfcrvyi9gKInUz2zk/Nl2tsQRt1ZJksicnYV/chL71l8JQFJ9Lb1nPYivpIC8A19SEn6RnxZfzOTRrxCb3qfJmwKyqn0EwwYxFX70UBBp72oUIDbSza5FH4HkxOl1IkkSp1x9BZsfzEdHcN1v/0Byr7bTMa2x9POPMIN25GbXngubP1/Dibwo3dL0Lo04aniqr4fhi3RAJeeuFViSfaEVsoSlSKDIoErsK2hEILH0wY/RHAqaS0V1amhuFc3jRHU7cHidqF4Xjgg3jggXWpSXQNi2ElY1b6fjDQsVZychcb9lIonjT0MGOFSxm1jT5NTJdx3xRO8UkqUjtOMzT0syguRlDibjnFOwdB2hG1jhMMKwXwtdp2rvTvwOi7POPLfDPpxxEdR5aiBgIWotXjwhFkOW6B1SuDjngJ0HYissicXL5HEnEJkQxeJ3KknTJDxfF3G05NYDnB6toh2j3g6A7EoCqUWg/UxcbHMn3122glClTO/iMzjP13Jsws4qADRP24vzvr3zeXTtX7CACiuMWxKojqbvXQh2fHwBUn0JlqwgZBVLVsjw1/HHUC15C/5G0BuNpDiQZJWGIKxTxtAQmYVbk3GqMpoEhzy9qFy/CzcenJqCQ1XQNBVNVXBoKmrTs0NVcDjs7CFVU5FVWwclSRLFSXcQv38N7oRytPofEWoUql6AahWCVUj4qdOxfvceBX8/mwH6PmKEQo5jEDlZF5E4+iwyRkwlXlaoXrGZ0IES1A4ckadccgaWGWbHTx9TXbTrmN9DRFPWXENVkPj0nz8N1YMeHC96SMq/CcpRjqWJGVn84Z1PKcvLRdUcxPfOQFUcFN27Eq3PyZjpXnZkZDOmMpfNLqiMDXPyoVlE6qnsHvJrBqnbME2TnM9vw5/0FYlA/XjBzmI7OmHIdhg+JLfMxRcvW8nz33yJLEM4UIOwfICKX3ZxKHEsJ5+SSLK3ggPKSxgfxZKX/HDT2EHx5PLVglvh4GB0h30S8ueoiLKtHO7tZKJzDmnTTqd4+XcUzvs7khyLFnE27/5zPzfcZt+B3/mnB485H29ZFnsX2wRl0NkVJCY8iWWFsawQeaUabkeQ29MbMCyToTGJzEwfyztp68iq0UlMiMAKW6BbYJhgCCTTgrBANnVMxcGBQhempGEpGqJZFGkBwaZHXZvxOCJL6HMaKP7O9REhNDyKRHF9AxtKysmMjqRfXAwRDgd+00Q+TpKiG0E2L7uX+SUrGGiYNOx7H0n1IGkeJNWDrHmRNC+S6kV2RiJrkUjCwJKPL1ojC4tAagbDrruuw+VWKMQbvzyXZM1D/zPP7rgPTWHoA7bosjA/DyPPPn7RXi+3PnRnc7vWpBhAMXehfVtBqddP+qzhmGETK2Rhhk18q3cSY8Xy4xu7Cb29DwmBLNFUfwokmab3MCmqF0JPYOfD60CCv0uPcm/yw9So9nSN0OPJ1Sy0IT70PBmQSM5spL4WVHfbqZxNuQvYQojTHUmkCUH/qJbYnD9QxfC9P3LI4SKgOlCEhSIsxglb76MeWohDGCjCQLN0vGaAWvNEbtWvb3vAkudCMXw4v6Bb35FHwDwpgugmLyJLWDiIpp7ZxN58UnO78K4tqJ+ciiyFcAQ3wnODGACsyLyJ4WfdyOD4tpEbfzjE1soiZFchDq29kNflcTH7mkuoq6zh8M7lxxxnUnYUDrfKzuVFTL9kYLf2rQc9+Hegh6T8D0Jzuug1aGjz+6KXNwGwZXIGD/RZx5kNezi9fjiTEtL4NFBEXMDOyElylbOrzknObbPoN7flZOeNKKFkVSkWEjUxM4myXPymdBKlGKxNd/PcoAu47Nu3yVJiiO89kJjkFAxd57V9xezwDOHHVUN4YtoDpFWDOymDo9Er6SD7DgwgHGwgQ4nFOzxA5fw3kYb+EYBLbrge45pr2J9TwIKF5cj7/PiK/W36OJYI78NnFkOTz+jM09pWUhaLV+MOW1w/+JQ2n7+X7UTOdLLw7FGd9uuffQk4HIz7pqUqtBEMEa73ozf40RuDhBsC6L4guj9kPwI6vvoC/ECcR0UIgVkfwLJMkE2QQcgm9URRbfj43eqtbHBGQnEDUExMKICQYlEIc/4Pf0JTVDRZwyFrOBQVh6yhKQ40uem1rFHnP8zy3H/ityx6Aw9WVhH5yU1dHjOAEcB6a8ox27WGZJpISuf+L1tfep4GVeaUKzrW4hyN+lALIT46ptN6qurQ8r0o35Xj8xiMuv1kVFdbn5yDQT9srCEqVsKSbIM4y7Lr1wghsFoV660INRDhikAJ2SQyQcTwat6TSEIghEmZZfC9cxWZ0Sdy6jN2GYH1X7xKbq2J7Gpr4+43/EQKeOSXP7XbN5+vAg/QMP02hp3YRX2fJux4ZBpZLgdbbphNo27iC5v4dIO6hkY8jQ3ohkUobBA2TAzTJKxbGKb9WjctDNMip0jn46IwNX1MNLeJsCyEJRCmhTO57dgdQ0fDn8oxS4vR5z/Jvppq1vrCuCPPY2p8+6mlKe+ei64expsNhtx5UUVhmm38k4QQNFT4OLRkJ4Vbi6isEsS5/cSnR5AUG8POZUVkDIwie0z7VP4e9OB/Aj0k5T+EQxv3cKbnSta6DvP3iN7s2V/Aneqvuch4ia3BOAakydw75gEu2/xnyoNJ6P73SJnQctcv+cG15CaILCc3MZ3qhPFUAwtC61F9qXwy1IVflXj17Ct5QvNzyZQWXcpbD77HkZj77cv/zEUjKnhs9kxSycXQfYT91azZNINe5jRmPvZY83pGQyOVfwGXt0U7oGoqQwb34celtr4l2M36OkdQe8C+aF71VHuBaECAQ7TvL4Agga7JjxTyQ2TbO2fV5UR1OSGpvWj1CA4sXU2BBXml/+DgN2/i8+5ss9xCAulTPmlIBSf08tVxR3YKeQ1+tusmexoDpAYqOSCvRlgGQhiAAeLIw0TiKBM2R29e6j+d8WlTkRD4dT9C9yEMv/3QA2D4EUYAYQRAD/CPFXXosYPpOpm6LWTLQupEx2IGAmxcuYSUiAiyT2mv7egIbUhKJ1/H/vfX49kRosYbYuBN09oRFACa9B3THzwXd+zxiUOPRlpNA0ufW4Ea15JubOgBVAw4KnPHbwTwdOLUGvDbNbYcrs5/K60hkFAQxDo1YlubFSZ3LsQ9ekr4u+9z+LhoH/3OHE9KWuf6mdZQUtJQrn0aI7eKv72+FjYW8cWuSs6cobIveIC6YICd1evQ1cPN6yR0of3RKysRlsQrVy9ASDKWpHAk59sbCBFwxlMfiiP/YMs6S19YSdYb53VJgHvQg38XekjKfwjugfGwC4p/GMAfx1/Cw32fI1a1KKxSyCpqoEI/A59zM+M8Mt+LIgqSBxBfX0F80/oRixVezrDIrIPoYIvV+G+99/Bq8SSuX9GIMjqRCi0OUVtDwbYXkRQNIauMNpzUSaPoFx3LoloPS3Mgb8nfULUoVEcESpMeQ1banszqq2wi4o5tf+L9w3Vjeenan4hI6dyLosvj4W1/caqzQO6gTktIAvcxMnekcBDcxz8Ws972dgmp5WhWHFmuW9Dc0WDJSEIh5+A6zpAWYtXFMW7UJGYNGcLA5JY713ve+YBwVSVP3ryow/4tIdAti6ClEzB0Ht32CR/5R/NY4BBpBUVcmTmQEzOPXVflwxXvc4qna8fZoyEJC6kTZ9PNzz5FgyZz6m+u7XZ/9brOkYrbrqM0NEIIdr+9mui9FoW9fIz93Uw0reNSA1bI3g/Nc/ylCI5GwG+zb2cry3ojHELDbKfzCZhBPJ2Q3aDf1r24PPEdLj8akrBaTSe2x5qlm/hi2R7KfDoVhsJ+JRpDUfmlVEQFDm69aDIh3f6tu1zHf7Ef2zeebXeczIVPLmOHspmc/HftBULCK2UzJuoCegXq+Ur/HlmXWJtzkMd27MMC/jCoD0PTUtFNk7Bcj2SFGZ5RjySBLAk8boneJw4gZuw0jJBByZrdeFPiUJwq5Su2UPv31wnuHoR7+PDjHncPenC86CEp/yG4HTYBcE99ikf73sELB1/AqMnlx4rBLB8/lAEFWVyRFMbj9vHJAFswuxYYvyKVSWWpbEiZyKIRk7l71zsscM1j/N7XublQ4tUcO5RbVBfB2Tkb6RdpC0FD9SqmkJER3K/pDDfz+GPt7wHwqo0cFG9AGNqUcylvW2elvszuyxMf0+Zz3TRZnr+DetkkKt/Po/csR1FlYqkkXS7H61RRNAlVk1E0GUVTUB0KiqYC9p3q2nnf46srZNRpp6ALL5fuz2On5gENdh6qRpFlFAl0Q+CXBK5jOHpJehA8x09SEiqmELH6VFLuGYMa1V48mzziXJY98ginjRjIxHHtUzUt07RFPZ1AliScioJTUYjWXDw+/nJ6717EkjqFhYF09u8vYFlq2379oRoeWXE9YUvHIas4ZAd1ngTmFWj4F7yES3HgVl24VSduzYlHc+FVNHpVmsR7E3C4XaguJ5H+emo6EE3q9fVs2rCK9OhYMo/yRbnz/d+xx3eAa0b+jjMntp2Oa9B1jkz0uI4ijXu/2Ej0XouKPjoTfju7a11SyMQUAsXx871EjiDot7NUWpMUPRxG68BK32+GOiUpoYD9W3e5u0dSAqEQjZ3Y9b//6nzuzdOAWEaFy+it6exuimh9INIB+OGjA5zgCAFOXO6fR9ai49x8/8gc9hcO5vzFNklZOPc70uNt3dof370DgEm5jbZpXYw9RfOr0gCUNoVGzrqC7MJcbr6s44rMmqqSMWtM83s5bzdm4+FuVyjvQQ/+VfSQlP8QCioP08/fm81VPzJKTSJFxEDMWKKXvoov8WIAzAIntyXIuHx9CXpzAWgYVE3fqSWUHsjgj999w42Rb3Gd0Fg2LoUtS7PabEMefwIVhzfgLw/jTPLgPe89/Hv24Np4A+coq+n3aQFBRSP9lj8QO3gJRqAGI1iLb9cWKl9/Gf3wYva/MAjFad/hGZYESDyw80Gqyz3IyIBCyGrEUsvp73qYLOEi1lII+XWSQlU0YlGnuDAsBdNSMC0VQ2iYQsNq+rmpEdtZuKsWgPWvvs3bE6dT2yoTY9bBo0SHHpmYcNd3m/LPJCmmL4SwXMideHZYTSfjzqrsWrqOdBzFCR2KxvWDZ6Hs/Z5dFSa9HO1FtxsPf8WXRTtJ0ey4ki4EWpwXSTL4sdRAktuLfE/aZnHdd/ZYj3BPF6B42u/Xhqcfw6cpnHXdTZgNFdzwj7PY6gkgC4UGZxiccNeuJ7hjQTUOPY7YGJ3+AxPYakgQa6fsfqd4+dWi5zhH/ogIwyJS9lI9QUNxuNjw3d+xLAu1fDQe42w8SW4kp4IzwUPqxBRCtSEU0XXBy+4ieCSS0spRV/fVoYn27qh+M4y7k4yicKApaujtXuqwikmF32Lel3twaAqaJqNpMg5NYbvNd3h9kpdTzrkKgBULVpFXWMXEKSO5/I01lGmRrAnbhE/kF8HgrG5ttyMM6JXJA9G38ue6p1i7bgnnn34ZAM7+/RD7VP4g+XFLMCAuFk1WyK+ttf+SZZnnq8NY3TSN82/eTPGdd+GdMgXXsGE/e7w96MHxoIek/IeQJKUyqLAPuxu2oRjp3DzhCUYfCNM/A+KUXVSbQ4ntVc6hE8ZQqz0IRg1vBa8iozpEuFplkNlIqijEFCqvln0C34Lm3Y/u+waAWen76bO3jL/nTyIQ0KAA2PggimTxu/4qgRwnirAojvewYsFnJH/0NqMLypsM6sBhyXj6RuMdMQCjIYAkS5RU5zLfHcSbmomqxGJYFgILTZaY0+f3PL1XkDa+nvvPOxWrfB/y389ha8qFjLr2jQ6PQWjHetRPT6Nu2lsUy2ls+WEJeCI4a+tKYgZOZvbooViWQBe2kLJsdTFSZQBrZgazBnV98ZCNEHh/znSPjCQrnd75K01REtPseKrFMg3kDqIVneGAL8iF65dTJnpxsvsQfx9zSrs2lU36iPfnzicpsr07jGGaNIaDNISCNIYDNIaD7NtzH7CN0LOvYgRDmKEwhmEy7bTpbdatP3SQ9Mb3+E3fIHxzAcp39RSnp+JzaMyRx2NiMiRpGM9tCuMPZuAHahpl9kQk4QqHSGysxaGHqfFE0lfLwYUfSR4DOjiCFopLwRI6jRGrUcwaktZOR8m3tVVG2M+eK05CBcKyRn71HyGuD5ak2LVjJJon+1q/F0ggQThcxba6ddRb9bbYU5II6QHcio/v33iE5e+7QJIJNdagShbf3XYekiwjSRI/ysP5JuIkTMViziv/bC5tIEkgyxIj63MYC3g83SMpsbKFU4T545qDHSy1SWtMTMvFf+ppJzK16fWyvyQjI3jumsc5rIepejuKtEcy/iWX2qBbhzooD1c0fxa2wgjZxc3TTsStdNz35g/ms70bWWN6WRnFt9+Bs18/er3wfI8epQf/MfSQlP8UAkGS0xpYYM5igFyCp3wga7PCbMkaTWTN9zTyBcuntdQOcdbmcdpu++5OFwpbDnopHbIJyzRxOSoIhhNRHAMQ1hT6Rm5hZJTtUpmeppOT23JnbwqZt0pmMqioCHVYEkljRxOv6zi//NZeHhONQ1YxKytI+N3VeM/6TfO6e9Y+zWf73mLJaXeRkNg2jdEXCvH0lz8S3eRy1li8lyiAuKxOD4GsB1Ekg8iUVIYNHcewk8bZ+6frHeoX9q2qwFHVQPbYjk3hmvcxFCY6sQ6l6DP8H9Qhubzg9CJ7IsEdjRQRgxwRixQRhxwZh+Rs0cNYjTEAHLpzCaYwsDARCCwsLARBIcALC3Ys5bO9W5vyY+0LpCTJKIFqitQYRjy7hBtO6seVQ9NxdmLN/1V5DdftOoQmIridv/KbUa8T7Wx/F9sQbkrzdSd12I+qKMS4vcS4W6anylGpjlY4cc60Lo+V7x+/JjOiFoCCyPHUWSZ5Dtvq7YlLX2vWcVx6ssHoh78iEHISHhnHJ0MimZYypk1f3yz5PZrQOfXUt9p8LoTFT0v6g2MavR6ZQrhRx2gIs/e1ZUQAQtEITbmYiOg+hDU/4YxetjfPkYeg7WshwLSIr4kjzh2Hoch2+QLLAg3CIT+aptrtLAOHouI2amjwSQgB/rDgs8TRqL46TEcjBRVy0zhBCLvWb3/DYyeddWB81rxfloXwNyI5PWRK1aTF9GbW72ahh0zCIZNQ2EQPm5S9uxoaBGOnjemwH5fH/v39MqmO+gWLIHsORXevQEuoJukPZyC3igpVvL6I4H6lib2JprQnE2HpmFj4IjwIGcKeKkiDv9e9xcdvfYWCQp0SQshOuqITBh3rwI5G1RtvohcV0fv115Ddx1cioQc9+FdwXCTl5Zdf5uWXXyY/Px+AoUOH8sADD3DaabaBWG5uLrfddhsrV64kFAoxZ84cXnjhBZKTe0pnRrh8NPY1WVBxH35cLN89iquutbUnf3wjh76lOsLcxoqTRgIg9qVTIuJwG2cTaEgj+fC37A2n8IKaxBjzEaQIGBDv41ufQuR6mRtPSWNsdAOPDDuZa3LXtslhmLJ6J5oloLwaa/QYhj7wF2ouvITSc8/HMXAk5pqlACiJfduM2R+2a6B4Osh4qPDZy2Lc9gk3atTZVMyPJVhf1ekxEE3urpKzrfbjCEEJBYKs37qC7woWMDR+KGODYzE7ycZojVBpKRkzqoFq2L/9mO0XFA5gf20iCuBUo8iMGkbGhPFYuoXQLYRpPyrLqhC+BjLiBlHg8KNKAkvY+bGiKU+2RvZyyIihvtTP3z7czoqJVbx37qh22/xzThF/P1yBS5Z4pm886v79zN/+CFdMfLJdW7/uQ0HgVDs3mDsaIhTEcHR9dxvc8QOp0lYA1qRfiZLQnwnn/h7etgWQX634EynxA5kw9BJcmsqeP53Hk3vX8GSJE2+TpsqyLC79/FvKZME9MbX48FJUX0SiXoeQZRwxAzFN+3v2uPqiKDLuaCdEO4mKr8UC1Fsepv850yh7YgfR/TUSrjy2LXuo3kfFI5sZOGU8Z/5i+jHbt0ZRYTF/fnEL95zQh1/P7bjswKZ3dsNBkP31CHcEkuZqJ7wNvfsQrrznALs2pHClEhHhhKN4plv1Y+A6ZmTECutNpKMISU7HqEqg+MF1WIFCEq4aR83HG7ECHpA0HL1DR4odgxA0btuJHAwjZp6CMCyGhodyXmAOldE+DMlEFzqHDIPKqOEoXZAQS4BiHZukuEeOpObdd9GLinD26XPM9j3owb8Lx0VSevXqxaOPPkr//v0RQvD2229z9tlns2XLFrKyspg9ezYjR47kp59sH4L777+fuXPnsnbt2n8plPn/Avz1Pr5b+kvqpXH0duRSNTie+998nlfOvZRIv49hRZUM++RR3EW3EE4ZxGzT5AReZCVR4IF+voXcdeEf+eS+G/luZF9ogOm9VnDihjR+mvESgypB3bcCZ+LHfDZD5UpNIyu7itsKPbx4ycUEos9C08Pc/t7reG4+kUCfwUjQQlDSx6ImtA11B8K2YVZHGQ8VPrs4YoynJSohI7DkzvUZR6q7Sp72F98d2zfyyy2/bn7/TdEPpIXgrrxxxO7oRfxwe9qjcvM+Drw8D9UM4p40id6zJ3AkgaV8zNPETzwFy1eP8NsPy1cLgQaEvxZCjXgOPEtavBdP2kiMUAgjHCZ9VCqDrmifhvvsUy/j27SCe//+aaf7BHZmy3s7injw692sXFfEmIJaNt80AwDDsvjFtlxW1/pIc2r8MHYA8U6N98svJqH2A8r9d5J01BSD3/DjON46RMHQMUnKhk9fY6oEIRycUPRPKILi8Wc0L7837zPIgyVpE0mItS9EGxvtv9vztx5msLYBpwRr47NJq8gjP9iXe92Pw6YK/ikuQsPWyij1kyAKHEf7lHzyHi4gZlhfjCr7txDc66LqvSXEX3oSXcEI2NoT2XH8wd+qOps0xUZ07tg77NB7AEjP9ANACBlQESggqYDAKQIYci/MEb9H6EHUiXM77kw0/9clRCgEkkSvxy+m/Pn5BHPCyJ5UZHcvqj8sBXohrBIcvU2Sb25bN2rLn3eifPIlIz57uPmzSbQdz6zlWwiaEhuKSpmUkd7hGMJhHWSo219gkzLJ1qGVbtxP3vrDRMS6iE6NInPaMDzjxlH+xJN4J0/ume7pwX8Mx/UXP3du2z+Cv/71r7z88susXbuWoqIi8vPz2bJlC1FR9snp7bffJjY2lp9++olZs2b9+0b9fxCGT+UTIkGM5Zlgb1Kdy0lNSOeWH8KcnH0r/j22oZdRtpMD/YdgpMeg7armExFkoFzL/TdfQ8PGEI+PvYS5xd+TlxTDk8HzOf/ClVjFh5G13tTFT6V/+TbKlBg+CI0jvqEU4r5AMZumjTQHn500h99umI+naie50QmE6xxszBjM7kkD+U1jBe6KAhyaE4fmpDJUb0feJaldTkSlzyYwca0Ih4MQfQo+Z9cjazBkJ6biwlKcWIoTobiI9pcyCJBcbbUjNTXVzQRlihjP3OwzuTP/QeauM0nZvpbyC0+nWHERTBtIxOFtxBxZcfVnFD8NIiOZIZNBjktDSc7oNLwtLAvxp2eJHz6OkZf+7Zjfme73YXSjZoskSVw2ohdxEU5ueG091SU+7lm8jz9OyWb2xv2Uhg2mxkTw0cg+zQLckwZcxcb1H/PTugtxJf+SadkXENdUOyloBHAer6g0EMLsgqTkbN3Lb/y/4ldx53L/7RdS/bdBxJmVVL7za3bcswNhhPl23VPcnfMB8zc+x29PsSMGDw0cyI0b5hNPBZbcB39eHYnOerKN9/gp5wRoykJtrEkkNrYEtSGSsDiM2ZhBcp+24kppyBhYcQijth7nqEHAbsCBf3OQ+EvBChoED9QgawqSS0HLiGy+uTGbBLKy8/gvjlX1tuFgXGTnmqU1sX9hZOkq3CdMQ9IbEWE/kqkjLB1MA4SE5HCgDJuOc3jH0RiA6k+Wopf4kNzH1nlYTSQFIOkP5wBg1DdS/vRXGJUGoJJ8+wycmWkdrtsVUbAsiznJcewsruGc3ApmHF6NjECRpKaHjCJJbE6JxRvQeO/pnA566YXSEMIscsDGXC699nqKf3sVdV9+Rcx5HZdR6EEP/t342ZoU0zSZN28ePp+PE044gdzcXCRJwulsuVtxueyQ58qVKzslKaFQiFCopbpHfX39zx3S/2qo7lr8VghNdjJbt/igJoZYGqite5F5tRIbLooiP9VHb/9qemsJ3JG+kberLuL5UpngrH48u/FPvGSdgUct5+6xt+LVdPooVXwdmExtspvhLh9rfDJmdYumpMKXzKMjPmNjTTanb/orExp/ICSiOThuMkMLllI7M4YNjQMZEllAoVnEzAoPodrqlkF7rieq9y9ZuGQ4kgijSLZLRn3UHILyLwGIa5VRs2vgH5DLdiCbIRQziGoGUfRaNCuEZoVRzCAbxABGOCNxAsXlhby9/A0+8H0GwEgxmOcvfRVN1Th9+nl8sepUTOUwiinweZLRwgFCY04m5bZbie2TxKH3viHw3mtENRwCQFa6rm2jN1bjkEA6yi69I1iWhb+sCLMbJOUIzuiTyD/HprJhUwkfrMnnfTVIyBL8PiOJ+/q2vdCkRaSSOfwDtu55iLjSJ/ih9C16DX6ZE1NHsbBkN7Xmse/EW0MKhTE7Mk5rwm/e3kjYGcstN5xN2F9HnGn7gowIb7HXVx2cMfku7s75gOeKf0Jb9AeuOPV5SsqLmVn6NeOTt5LwFxVvhYXisHj1iqkMy/yR3BVZuAJBTnDuY5tjKgPrbwag16NT243BueILAFJOHoesKfR6dCYlD89Dr7KPccVr29GLW6XByxKecclYdWFqA+W4UPHV1x7XcQGobbTTlGO7ICkV0hDKrD4MOqP9uLsLKxjGv1lBie2P0IuO2d4sLwfLov7HdTbhUCSQFaJmD8U1pA9qTOf6GCsURu6EpCz+yZ62HRrI4o/uJH7kVGqteEwkLAEmND+7nPWMMfYxc8aJgLDLG1i2XDl+WBYJw7NY89h8th6KYd6bJViT/4b7ywZ+cZqB090jaezB/zyO+1e2Y8cOTjjhBILBIBEREXzxxRcMGTKExMREvF4vd955J4888ghCCO666y5M06SkpKTT/v72t7/xpz91XYHz/wUU+mW+8KwEYK4Yy8TG/aQmTeZwWhQxNenEN0biqlrPxM3fI8ccJC2zkMdG3M/uEcNQcxORsmt5aPyjFCxN5fysFewpG0dFqDf/VMeDBcOnqKz83g63e9RygkYiN5kxjPnhFU6UlxPv+B4A7Y9b6PXRCYQUlUUp07in+h8ATGMHIxPOx6NphA2DkGVwV72LKikGM/pUEAb1gcMk6TuJbVgILOSRKQkkeT5u3sdJl9zT5TF49e3NPJm7lS16iGWrvufWnHubl90Ufy2/PfOGtiv4g5T1iWHm16s77G/A7y+G319M9ZsPw+En8EZ3TSj0ugocgOyJ6bIdwDNPv0pExQHqI9vfxXaFmCaDstCwWGRL8PrQLOYmdby9kYnDGJn4KXm1eWzYdjU1ey7l7dxLaDRs0i6EaJOmGzJC/HhwM0sP7qK2aCeDdMHlc28kMb0fckhHODsmKaZpcchp64pURcblTSD37C/p+6Vdr6fqkSzqnXEUpU/hDu8wHvft5MnSJbz/3HPsL+kHXMU/0Nk+8be4JDtl+rLAYkojHZxf8AVyg+Bt51QuT7MJiq+DMRyp7QMgtxJJi7BlO/MCVsAAVSL69GwCO6oI59XhX18KgKvpVLV9ySKShw8gcUj3dRE1PpukxEd3rvERIZPwvzgjbTZ5trgG+om74hfHbB9u0vYV/f7Kdssc/cfR9+t3O19ZD2N1QFICdUWklQTxu2VqY/IZTz7jWU9Gysv07z+7XfsfvroSS9Qy6OK7O93UmKtnEn52AUKArErsq0xj57JCxs7JOtYu9qAH/zKOm6QMHDiQrVu3UldXx6effsoVV1zBsmXLGDJkCPPmzeO6667j+eefR5ZlLrnkEsaMGdOlHuXuu+/mlltuaX5fX19P7969f97e/C9GbXVN82sZlQxHFo311VwmT2JhyMHQEOA7k2FpY7DMajZ+UMnM9AZ+nbICV4PMG4cepFqrxtF/CSfn5pMvxlIuWk66L3/f4p0h932eoQVXklTfG00uxqN8hXFtLkpCDKqqIushAumZ3HPVM4TeP4DzwAp8boUZI07G05ROa5ghrl+2DST4Qp9Gyfoo9hXbF89+8TncOeYFkj2VpEfFdPsYbK9Yjqvvm5zw5TPNn01QR/PMWc8TFdm+H9kfxOpAv3I0XG77AqjGdV1PRG+woweK59jW57KsYAHu5K4zi47GgHgvPwDusiALTxnBgA6cdY9Gdkw2CZM+5935v2BAwj84I1rly1oHE/4xhgwriQQ1icNCpVDag5BtChAdEmxwwXsLfyI2pPDSbvu72TNsOEpcHI6MDNzDh+HIyOD36xvBaZMt02gisv0nMSf0KLPkTUwclYajroABB79hSriK/kmjubL0l+wPpyE7ypDUBiR/ejNBAehT10jiLpWtDfax94hBzctMQ2fL+3sY/auWjLCiH9cD4I9uezzrK0vxRoxk673zSTBt7ZN7QjKRk9MRQqCX+AgdrKVuUT4HqjdRUnOAd/70B4YPnUlEUjwZo0bRa5ItvPXX1/H5ow9hGjooGj5PJJ6YOD4sjAIpmQc/XUe5z+SRiycyNPMoMb9uYRyvDugoiIBNUmSn1i0NnnvCbEJ7tpD29LNgiqasHUHJPXciQsGut6XrzSRFCEH23d8BkO/6JYOBkCaxcHg/PN4aZAkKDl9H374Hmse1fPmD+P0H0CLW0VCRzVvPz0NVVRyqk1lnTyY+OQbLsmio91FRVkdJpotQKIzH40EKCuorux7fMWGZUJULqgMRnYH0X65Z7EHnOG6S4nA46NfPFpeNHTuWDRs28Nxzz/Hqq68ye/ZscnNzqaysRFVVYmJiSElJoU8XanCn09lmiuj/VWQkurmz+kXKfI+gJkaQV1PAlyOquW3qWdz5uU1gxnkU0h2ZCJHBLmsYq33gq3+T2G83seT8L9nTqwwsmK0qvOufQZ5ln9TT3Huo6/UZCAVJ8SPJBqeNf5Eol8VOYM3qCxEvvEVc1VgA1NAFSNIelD1no6gaITEZR1wjV7b2UhAG01lMmZTFwsBgXMUt4eucqn4UidtI50kijxJHdoVALxNCcFpoOuvlbVyfcTW/mH15p+2VQBgp+ti+FaLRziiS47smKUaD3U6N6NxVtN4X4MW/PYXzwFoAZl/YsRNnZ1iwuwzZo7L20okkeTv+XR8dIQGIdEZRtSCaDcMu5ZcnD2WgvIftlQUc0PPZFd6NYgoStQn8Yuhc+qxeRq9/fs3hzFQOXTqS6nA55Rk5xAUVvHHJ6CUlBDZtIrBxIwB3yAp3n3gte+Myue7hT3nl/l/wfUEte0UG6VOmcttptnYkbBps/P4JzB9WMyfUG0NdQaNjO5HhCayIr2KI5wOuUb5lWGk+ecFI3i4+jWs9/yTqUCypvu2Qbmf5HW7cQs6GOnZV70Z1O3B7XURs3k4akPLgA837bFkWwW1foiUUUpClEOU9FYf1/7H319F1HNn6P/xpOnzETJZkWTKzY44haIfRDs+EM4EJTmAmMGGYZCacTJgTh8GQ2DEzs2QZxMzS4ab3j1ZsK5aU5HvfX9bNXX68zjpyd3V1dXWfrqd27f1sGVWNIMsygiBgS/NgS/PgnZRBmj6BmA/6sHfTavbuWUtkV5A1S+ZgF53YvR5Ep0JrbTUAanQ8IXs0Un0jNtEFJiypMtBMiVNe3sjMXIX7zx1LUmwUgiAgqga63DtJMTQDsYfwcoDwXkuAUWuo77WeQw8CGP5mvJO7hio3vpiLEQr0cJCFspp9xMh+znn3HGRRxpFmB1PkQTEW2QQFkx+3nkChMpE3x1r+bvPnjmPylO9QFAeq9j6KDXztSTQ1p1LXvB/D1DAEjcKXNgACHK6oa4rI2NGEIDghrvk4oH+3bftF7P4a5t8FHda9WlSTR5VnLPHpmeSNGUff0WOx/T+kuDiK/5v4Hy8qGobRxacEICEhAYDFixdTX1/P6aef/j89zR8eZoyTJ2JvRHEHSawpJpJ4NtuGRPPh8stw5thZ15rP3NB4Lmh0QlwfFEHDVAJ0jD6A2gcKlbqDdc3MTKej8NBAmx4QuLvuIpKDSWwSNuAPNNHv5GUH96cKARojFrGMVuqoaykCBJKznZi6jq9IQmtO7DJwyrKb96ffzZa6LczYDeed1cxJ3hPZVtnK2SMz2LDzBSIRe5eEab+EkKgiGE6evPqFX1VeCWkInl9+WYmtOwAo3bsTm8OD3eXC5vTgdLpRbIeIgt5h+dvInrgj6tDUCI9deTmuUCsKIuqAyYyZciwjhw86omxvCKk6LrfShaCYuoHQ2U/ffbiD3MI2SkfGkZwbS1ZuHCUlzTTuqcYV9BPfdxgDR57NwM5xq7Gynndus1RLdWcOxo4Iud4tiHER+lRXMU49Hu+1zyBcd2QunfD+/URKS7EXFDAvM5PvPljA7VuiGfT8ShLjLAvPn4ccivqwSTIJLTnMDYylHzD9xy9ZPPVFAJpigywemM4XtSezw1dKVuVSjpWW8WPGJIboWyiOi6ePcz0NapBWr0TEvY2GJhBMGROdc+Z9CkDCtNGouopu6rS31mNvqqFKWkjezfdR4QvQd1UUdlv31idREhlx6ZmMuPRMq292lrLrpW8p9xfR1F6F3mZZiS596gWW/PA9u8uruPehh7vU8ebCzTz6YyXzDsC8J1eS74kwIMnJcJ8N1X3I2bWttplXXnmFiKlhdErKmZ1d7MaBJIjIooQkSNa3KCFGNCRFJmabm+BVHyAKphVAL0BzqAiHvh9F0BAFS2DOaKgnLiWJrgpEnZmJxd4dhHUlRFBR0dAI62FSnS00ahJfKgMQdT8RRwtEf0cU3/GPKicPxLhwuJv4YuuD9DHGWNfheojjpl/Y5ZmpLm9g05ptGIZBMBBC1w365uVQMCybuIQYli/YyOK137GrZQmnhY/57RPMqk3w2RWQdxyc9TK8ewZuRSNr8DBq9hYx74WnURxOBh47nVGnnEFsym9bbj2K/3v4TSTl7rvvZsaMGWRlZdHR0cGHH37I0qVL+f57K7naW2+9xYABA0hMTGTNmjX89a9/5ZZbbqGgoOD/k8b/kVDWUUd0bgDbmsmoDlicv4/GxL6ssp/IpmAC680hTEhdT/Vp7/POztksr57AeVIb/Tx1HNh+HWS9C4AuJdOc9hSOwuqDdZ8mjGBEu7XOHx2fS+XULw7uK9w9mUY1jpigZZHwH5ZEsGSPSSRzKE73HvzdOIh2qCFO3a0DMgNjMzg+P5njB1pm8jW6n7De+1LGvc+tYWNDBw5RwCGJ7E5ZhGkPcsf7N6EIMrIg09pWjeK2oUgKimjDJtmwiTbsko2TajSo2cGmB+/Dk5iM7HAgut3Ibjeyy43scSO7PYj+/TgFyJlz3BFtUE2JMDaC2HAaEZDgnYcepNGZCbKMINkQFAXdMIkOtQKwKP8c7PkD2Vgv8ubnW7ArEg5ZxCaJ2GQRuyziUCTskohDFnHIEjEOhVSPDZ8vgtw5225vD7H4413kVwRJv2M00VEOhm+3zjF0XROsayIMpHV+Bva5lbnfzeG1HzcgSJYQWXv9HkBCdk3DbhuCYQjsC09m2jfvUnfVOVQ9+R7S8+8jygKmYSBHKThf/4btG4uZctqxRHVaPQFOvehk9NRN3LimifqaAKZTom/MIRK47Kvv8M35lERxPA2JI0CycXzzappisnBsXU12k4IbL2d/8SVfTo7jjTMuJOLMo8lM4688DazCHXZSsuFqFNnLuLHjGHrcaCKBMPs/+RiA4lEjMYRONVnBku633XQlA6edzZLPvqZCV2ndVkdW31iifsHHqH1/NX2jhpM0Mp8BV1oh5IZhIIoivo4OxG4yal9+wkgunTaMmY98TnHQTbHPRrFPZwJgmIeWMJoqGwgSYUzaEKKiohAlkdbWVux2G5qqoWkamqaj6dbfuqnTqAVpkZrJic4hRjUtLTpTsHRNQlvxAWmyw7JYYNLuddPWXci+rqM1NdLwwgeHwoI7JxDOEQPxjBtKjNuN2eHn60u/7rZvihoquOfHF9kbnovPEPhHS4AbFYFM5hM2vkcRYceBFYwbd4ikCIJAep8k0vscUkFuLq2nvboZh6IQ8YdIbNY5KXYs1Q017Fy6mVEnje/1Hh2BNS9CTCac/x7INsK6xISEUirrPyE/RkS45XZKyxvZvmgB2xfOJ3/8JEafcibJffv9/yWNwlH88fCbSEp9fT2XXnopNTU1REdHM3ToUL7//ntOOMF6qPfs2cPdd99Nc3Mz2dnZ/P3vf+eWW275/6ThfzT4XMm81HYyp8sa1bLB1rp0zl77BWfqP7LIfw1IkLO9DXOUQG5sKcurJ/CUcjOsAHiYK0pg+wAvT3ZcxyJBwIhSENtVhigHKNCz2Ca0Uy22kCjV8FNMgNLcjzcyLuTZeB97vfNR68bjTd3ND9r5BMxm4iP1DCjbgt2I0CBksLS8miibjEdRiHHYWddcgo7MM5ktXJjXVcdC13yoZu/Kkz/UtBIjS6S57YR0g2g1lxZ7HZVqFaqpEdKDtOqNGD4TUxbQBRNNNNBFE0M0+Um5xPXhpxh0zYV4OOb0m8HWAfk8dt4Q9JAfNRLECAfQIwGMSBBTDbCqsJIUoY30xlI6Mkcj6waGqmJqKkY4SCgQwm9PYWnCsbQ7UzH2t2AYxkElVEtIy0T4FUE3zgw3Ed2g6JmNjA5ZB6z+cBeDT++HAOzOcaHkRhMd6yTcGsKMspH8aTF2ycYZiRdhmiZ7fXsoUncg2vJRHBNpifaQOTiO2E0/EiEawe0h9j8vEXz3SarK6mh2xBIIOXE3+rj1tV0AFGz4nO//dVGXtp0xfRRTBtZx5fcrWZPeh/cXLOCOcy0H2rY5n5JYHaJ05EhEWwfhvx5PwoEpuAFn300ItUnkVFkz57NWNJMnF/Lw8TFkxh7KtSSKOtffd1OXc26u3MK/LpEYXGoyZdCpiAiIhomIQESxM2LGpRiGQW25SXMH1L2xixUOlVMKEhidG4duEzn2mAwkSUSQRIRO3xEjbGVlzjl3wmHntwiiYus5BFiWJX6433JsbWr30xEI0/rqesTDolUinbosx5wwgcTc3pcRf8L+ojLe+/gtBp89hlGTBx7c3lrfwhs3fkxi9gQueOKQc/kP9z9NadmR4oNSfCKR/ZtofOHhbvZlkb/qe/yBVlQzyKeLX6OitYKhfUYxdfgpyJ2JDPsnZjIscTh7Ky1l6ZAp8O86B0NdOifHxxGvN9JeK/D6vNe5YsYVBwlAR1kTlev2Ea7x4fJJuDokZKB8biVhRSM+5MaLh0z6oW7UMacbCMqvtKa2VsC+H2HIeSBb96eoPZFkhw+HUUaC2EjF3oVMvORpxp55PruWLWLdl5/ywerlxKamk5ybhyQryDYbMckpZA4aSnJu3i+c9Cj+6PhNJOWNN97odf/jjz/O448//j9q0P9V9An4CLhsfO45tNas2Y8jun0gz31rRTeFbTa+WTWbbUMyMU5KgmVd69AlgSeLnyStsgUVOK5vH0ptJpXF17JDsXJ2BDri6Vh9Hppm48zQODqmeMhd68SYugZ9xEJUQ2ZT3bFAPNCPlXETSAnXkWD3Mnt/92vpzvJbWbA/jGnKmKYMyHiFEA3heM699U0cgolDBLskYJfAIVnWhlYznnNMhb/97Sep9qld6l3/3iesWPQep1xxO/1P7LrPMA3U2SG+/Med1JXs4/KnX0YLBFA7OtD9fjS/Hy3gR/cHKN3QQIWZQv8xPWvxzL5rLv1TvCx46NiftcLCh7uruefdLbx8zTHMyDnSD8Y0TXQTQrqBX9UIqDoB1SCo6QQ0nZagSp0/wgN7KklIdLOxsJ6sUOoF+QABAABJREFUkMn+8YmoisiQ5XXw3DYAXIMTmDSxT5f6O7LiqFhSgcsp41tVTb63Pzl/OgklUSGj09fG0A2eKFxG2NHEpiee7jwyHzLyD9ZTlRoFnf6te+QY3n/pc6acNBa7w8aPy7aTleBh4onjeGP6Mcz+ah5PDx6LccPtnL5oLv3g4PJO1PGvsNGjMj6QjG5vo7nvd9ic+eT578Y3pAW5qZTxzjHc/fZzRN2yH1W10dyUycCBf+bnKGspY0+GQHTeWKbNeqLbvn3h9Y8Riy0rnWzCuX47S3Y388KBzmdy3m5rX+fnJ5vHF3hI7SaJom4YCL9CUC0+yk18lJvdmoSqHJqpR0KdiQt/hePzwWPCVsfbfhYKHpUQDYKHxsqyLttN00ToRlE56+2nMf0PWwT5p6goE/Zdcyv69pWsn3UeCaWtVCQKPFLxnLV/55e4N/+TXNK4ZcIdjBk0hfuPm811gekc96llYdQQeOucXYiiyLbSbfy4+0M61lfyaPmj/P3av7Po/ffpv7MPXkASwWkcIh8e3Y5HP7S0E3fZAJo/2EPD6zuIv2gAUtQv6MKoQfjkYrBHwbRDRG3Ys3ustnU0wdO54LDkAWSbjWEnzGTI9JMo37WdopXLaG+sR1dV1FCQXUsXoakR0vsPYvSpZ5E3Zlzv5z+KPyyOBrr/TshwZXFV9bsMn/Y11+pWrpNvnDL3fS/y7A3/ZMYXr3CgXy4l3mSKhEFEUEgduQj76npyhWqqzATu+e87XDZhCQBPx8XSYrNeYB0Tn4X1ltm20htkaKSA/VoZLmwktwcwTJn82A9JGpkHgsn+YyWql1cxr6qZAblxqAudyIJGOM2DT9PxaRo+TcevdhCvbSfePB1DDKEbYUwjgmFE2FvbyqqmoXhEg7Ap0qQLRDSBsCkSQSQsSETZBfK1ntfWHV7L5rNj/oIjSIooiNjtLmqrK9Elia2rlzH5gsu6rSdc8wmN7R46/AG8vSQZLKrtOGJbW1MdrQ1V1JW2kkQLbtNHIOTEYXMiHuYXIAgCsgAeUcKj9HxNj7U1Y7fLNCwqJQswgxrHnzGY+arOkDWNlEZLZPY/0nHXm+Jm4AX9UdUIpZuqQBVQ3i7j2/YAeReWkJGSzLfvfE/YYTn/Dk+ykZiSQHZ2NjHpebiTc1n5+hv8u1RF0VXW3zmVax/9gn+UJ8Nr2w7vLb6KLaThwxc4sbKUaXaZvIoYVo5/BEm3bFWGVs9tXkvs7gxpKYPUMnzNUegdKmMznuWAP41g6pnoYZGsygqKVsVTGZ6MkprErCFdQ28DaoCHiyyLwH3H3Ud3eOn6xYiGRVDG35bEiLxB/PuJdUwrDTA6LLPBrrLDESJPqOLsYZMJ+AM8s9fqB2m8HakbFdpIJIJhwvsvvcD5V1yJzd4z2TANk+1mKb6mIL5PoLWlhbLaCgAc3l+fp+YgSflZKLgoimD6MDVfl+2GYViJEn8GURTBe2RUW61s4rHbkEvLcMpRROXk8eoxVzA4awTz1n/GI+XPsYMyrlh/A38tuQy7N5ma2EwM0YtoWM++YRqIiAzLHkbWlVk889QzUAuvz3ud1u++IT39CrxKHLEX55AwIIfq+1dbYeKHQTsjCdeABKSrbTS9V0jD69tJvnEEQk+/i+YD8MU10FAEl38Prm58wtobkAHB2TXyTpQk0voNIq1gELbDchoZhs6+9WvYPP9bvv7Xwww7YQbT/nQ10m/ISH4UfwwcJSm/E4ICuBw+3EqQfso+9rfmkNZwgFfthewZ2JevBj2DJ+JjaGERp+1Zz7vHTMdTHUQFyow0rmi348u9ievF45Ac9SjB1aS5wkxNrifdriPmbqSpKQPaktlPGZIpsaXhR25ZamNbXF+C6ytIWLaHvJYERNOav50/NoXYydlsW1EMpsCwgu5MpxO7vR7xP8/S3tTAvf+6ssdrrrzjI0ytZ3G+oWfOZOknb+DxHvnS+gln3Xkfnz1yL231dT2WiXMq0A5llfUMLsjusZzUTYhpw0szyNP3cwtwiwOwXH/YFTeCQTct7bGuniBEDBLrAoyqtQasmE5CcvLp/WmYGmJilKPHtXU1FGb1c1/RVz3kLCiZCsXvhyimjECKZVW47777ug1x1XUNzRCI00PEJsTw7iMXcs0/57CUeC60NzJuQBo3bY3w8HtLmVRTSkFGNmNmnMmczduIFhtIiRWpaAvT3PI+YBELR1DD1zwWYf8ZtLoruTbtR/5y2nxi5Xns+zaLyvx02AketsFe+PfyhYf5UYj4XDo/pf/9x7c3896fvzyi3f1nxlH0nRXh9uObRazvu4ebbj+Lzz8vgiW1ZGoSJ8lzuFqeS8WwhWQOGMdrt39Mh+wl57ju8/5kZmVR3VHEvvpGDhQW0n/4iF7v2x6pClOAysIVuAUHCc4YJqfmY3P9FkuKRfLsjq5WBX+rlQJAdnRV4O0uyqs37BTbCffP5LZPvjti3+xpVzGlaQaCYXDVZ5fyn6Z3aDevImwMQUh7GkmtxR5Yy4j3R+BW3GiGRkSPoGQppAZSUdcGiTMNVmpLOF46Dd6touy4RuKmZNK+sAz31HRalADpY/ti91jEzZ4VReKVg6l7bgtNHxYRfVI2SsrPyFXx9/ChRVy1i75EThve7bUZfkseQHTGHNymRlQ+efAbWhuiAAGbvYUJ5w5k8LGDEEWJ/HGTyB83ie0/LmDxm69QVbSbQVOPZ8j0E7H/CumCo/hj4ChJ+Z2QluRlZmAF7fs8fKL+FSEooXocZAlNhNYIbGyJJ3axQhsOdgy5lntLmskcoTFHERlYV020aOWucar9eH/SJJQtk5BL2ijeLzLu0kGkC6eSnlFIg345a7blML/RzdW+1xCAsFCNHvYwTjoT8bCXYkdlC7GAqILu+m1OaaFgAJuu91rGNEQQey+jSI5eww0T+1jX7fD0rL45MjuO7+r8xMUcmVH4J6THOGkPqUdsj9GbWB97KurQc9nW0cqoRAfZKx8hIfDLiqE/h2GaRKkmg9p19qY5GHDJIIbHWtcmCAJJ0b3PyjdvWQODn2SX0sHYicuJcaUwfONOFn6yHUfIi2DIZOX07VGDozQoss1Io49pDfh2p4O3Hz8U4m3qOveu+wwpXEtIUph0+z0s+qgQgIwJ6Uy9eBIAr934Hbe+/SiBnIHYA8l4OnIRTYWocDySIeNMi4WY1eSdVs7uj/rSNvAknGuXYM8tILtfAbqmoqkquqahqREuKE3mo+x1bBW7k16H404dQWr+fua/sQ1d1WnbJSPLIrNmDeSBomZSasMMEkoByPzkBNYOfYi/jB7AE1sjDH14CUX/PAGHvSsxmHHebGLjF7Fg+Uqc7t4HLEEUMAXIzc1l9qzZXWbsvwU/WVIOJylBn5+3brX88oafdEKX8iV17TSpOhc8+COKKGATRWySwKWf/YPojkaco0+En5aDBAir1lLxN9f8GUELYfdEsMW5EUURURQRJAlRkrjZzGdRZTkLB8RT7wyTJpZTIafij70AU3SQoq5GFmUqfZVEpAhl3jLKvGVctieL1CEFxE4pwPdGCb7yBvpcPoaOlVX4V1STfd84RHvXIUNJdhM3q4C2uQeo+89m7PmxeI5JYUurn+FLJuHSD01Sdn73AsNvmX5Ev5mGQbBsCy5AcB+asCx6YyltjbFk5PtRHDLluwSWflBNxa4yZlw382C5ocedTHJOHqs//YCVH73Dms8+YuTM0znm9HNRHL+eZB7F/04cJSm/EwRDYzD7iORej7/qR2IbisBnCXM5VBN/kp9Fo1LwyfHkadbAXrFF5nhfHQP2zGHjqDsBiIQU0E1cyW9gS7AGmJvWA7j4x4iXOH/wRFZsvZ8UZ9rB1W57Uw00wfcZK5ihTKFdCBBlulhft4OEloGIGuj23yamFAqHsRtHRk90gSEhKFqvRVQ9jK2X1O8dTdYMyxXVsx5Lq9/yH4iP6blMUNVxHmaO3r91OeH595JvtmMmDWDitLMO2oxWl69lwL4vWT3/CQZM+DOx0Um9XsNPEAWBoCzgTHYz7bTBv3zAz1DdvIgoZysysGntRGqELBAkjrtmFkJkIHPnbMSIC/Z4/ErDaud4b/d9LkgSn/9lHJ/e+zGBjBFEpacz87p43rpjEbtWgr9tMadcP52rnn+R3duKWfJy5RF1jMmIZubZ7zH/qxHYotoZeMF+pk29hcj1N+GwdW9qf2jObSTXZzO0eiqPPPYWsiyhEsFpcyBKAoIkIIoC9jwR9YCJqB16FpXWIIJiYJrR7NTsKC6DETvuZfTfm3li6wIARt/7LZ9fewwFuV1FIIMBa1B3eXomr4dDUZT/Z4ICEO4kwY7D8vZU7C4h7K/GE9ePSbO6Zm+uU0ALCzhkEdUwadd01IhJdKfoYLikEEFS0MLt0NbE0PgoylITqG9ptHL3tJpIbQEsJfufooksVfsME+T+No5pPsBn51+AYRgcv+Jzdkedzg/THkAQBCJ6hOKmPVww31oqTp40nslnnw8dlkXInRSHIAjEnpVHzfuFbH9mM8PuGnOE9cc1NBHnoHgC2xvxLa+k5v3dXEgHX9iSGCm2szbnevqUfIIrWG2JuB22jNq+dwPSu6cQL3X6ACVmHdy3oqYGW8oB2juiGZgyhCv/fTpfPfghIyufp+zT3fQ57/aDZZNz8zjrzvvxNTexad7XbPjmc3Yu/oExp5/DkONP7iJFcBR/LBwlKb8TTF895RlOtPJ3EaMvJJR+FdVtu5G0euLaOmj1tLBySgthox6x6Afuv/tPKLLCmzffTLs3G4APJwnsT4/j/G/fpCi9hJoEkLRodMkHgs73JUtIMps4e9SHTA1EU767q8ZAR6SW170/dtk2Yl8lsi6iO37boxBSNX7pZ28iIdp6ttAYhoFmhLF3Y0nx+9v5fulH+GqtJQ6Ht2cC0h4IoxgG9l4GmJCqM1CqZMuTMxAEgZhQJX21KrZ5JpEyfEaXsmJSf4S9BhPWPcpau5dx0//yC1d6CEERYp2/bV1cV8PsW/VvopwfAdAsD0FVqxCMVqLooKziPU4cdy7zEbC7er5Gw+EgXvLx+IN/6rHMnA/nIApw4503AOBwO7jymRP49LHPKd2RwtaFmxl+wkgy+sQjOfegB7taITwe6zk56fRNLFnaD8E0qfrvnzHDHVgCYADiwWy6CAIz1CBxeyxrgt4eIehtQ9JthHQDBTuCIYIJgingDrv5KUOkEQoRE5SIbi0h/odS9qXEc8+fZU5u03lKlvhwVh4XfrIPn+jglFc2U/hoKop86DkOBS2S4u6F4IL1HIIlVPk/QWWx5Sez6rP9uNwOBAFq91m5kTxxCSx6/XOC4QgNaoj4vD44guVEbHbeuqdr5Nzi8CWkfvke1X+6glR3MkuWW+0bv+pmTlhlLfWUnzYBUQ2T8fYm2ksO0FZSRub0rvXMm/8VmaJ1rCiKeEUNNA3VULFJNppbmimrLCOmQyFk05l93d9QZBvV+63oMFei5R9iT1FZ0K5Bu4/GJzYy7ZYRSD+zqAiSiHtEEnpuFGMfs94xmwe+xcjZQxkH7HtoAfmRQqoeGoDj4o8x41KJjU5C87UQ1UlQamInk9rXylhp6Dq7s0SGV8K3A/vxbGICj358DX9WvrAesV0PUV38Ge4Tbyd6zLkH2+GJi2fKxZcz/MSZrPnsI5a+9wbrvvqUGTfcRvbQ3pf8juJ/J46SlN8Jkaho9uX+9ML/nLpdN1AS7ENsy6mcHqOQMPJP3GVa7/Y9pWN5+451VlHhXEZPuIET5I853nSzK5RM/cR4xtjgh8rJbGvOJYgOgsrykhhOKfovieoobGUZTCtZjZ6ezPaID8k0sTsFCqLdaJpOm6+dtNR0ateuRW3tIFb5bTP/iK5h+wXBKQEFwd5T4DBogRAmJrZunF2/+fY1qr+0nIRNTCJRPT+q7UENB70vK51pLOJR8TUIwFbHMTTLSZQlTmfKtf85ouy4adfhH38xPJ6BqPx65ctwUCUiCbwS8nP9Lxc/iI66QiqN1wDIMe/kuGOvPrjv3UVjsBl+lhX+m9jYGuxKzyqfumEg9bJqV1ZZi1i4CvuwqSQmHnLeVRx2Zt8/m1dv+Iq1X7cweOpQ1i99DD04E0GKEJPegBoWGXZcMnnDTqa6rZivvphFWcDEL4pc3zKXWN3gO4+bgkiEgQETmyF1RteYJGCygb9goDBxcAOjrr2kxzZ++MBa2pus2B1BUXCEm3FFLMvC/j42ZN3OjthhAEwYUcDGPsn868MlfFxpo+Dv85BMHQcqx9v3E00ARBHHL6iXRiLWM/pLJOXZdVuoDavYJRGnZGnn2EUJpyTilERKPSqhgELptuaDUTta2CIutfvWULtvzcG62teDHYgk9D3iPNMevZslSxYSfu8dNuRdAjbLqdiuGUSCfmxON5g6SBKr//M+W4qsyciwjf9lyBmTiS6w5OGCyDgFqy9N08S3P5dUr8Rr11s5xJb0/ZA9SevAC6N9fVE6w4KbVu4jljhsMW7Cb/wNoewdwCLQe0o7aLp7FdP/PIjEIQlHtP1wI4tNEmlu93Pu80uQA1dzanQJM4JzSX/PIlO19iT2Js6k3/FvYFt4F6ktKwhU7cGZlk/ej5sI9B1Mg83BgUQrlUIks5aOBokon/VbT1MLYe4VVC9+gejZz+Huc8g/KawbjDj9PMaePZtvn3mU1Z9+cJSk/EFxlKT8TjBtXV+UjtgvUdrPI0Ux0aUgZUtSaSmOAcHElqaDHEI3BBTDztqOixnr/YCMYj/fFI3mRf3Mg/XEOpuJcqi0h3TyNZHaktupBZAgXdvMnkAbHVEu7IZISGuitbQJwzQwTYPWpmJUw3qJjQjH/KbriRgG7s4ohvobzsS/8wCCLFk6FpIEsoTQ75+YvjAtz35AQ9IG9LgYFFs8shKNZHoRahJIdeaiNYWpWbsb2W1n4apP2LdvM+4Kq11xlx3HC1VvQ91qxjGj27Z0hH+ZpNwsfUY7bkoSpjL8hg9/8fqCwQ7cgOL49Q547Z2rFA0OkcHfb+GLgTnkZ8b84nFRaYOhGGyhNHJnXt1lnwgkCq3Q+gGDh4Bp7/lFq+u9O2J+9Po7mILIhVcemYpAFEUmnJ3Nik+beOuOr0jIswZPUTI459azsXf2Q0njFi6cfxk+0QSPtW3Zz0KAowyDpZdsQjlMINB7wwe0aalkjx9Ib9A0A101ePOG10jau5NQzhlERDfr8wXem6yjSUFyYw7VkRAXw52XHE/6vDX4wwJhVWBpSQfRUpifTDJfvPAPRFGyPpKEJEnIsoysKMiyQkiHsrhk6jvCHNiwDUWSLOE+SUKRJERMfMEQjwUEJEPCVAUMUQBM4LCltX45OPukse/O0YCAYZqY+rEYxtXIiowky9z24npWlFfz2U3j0XSd1OTuBnoB7x33UffxDvyiZQVSjSJEU2fN59/SXt9AXL1CS3Q2xeuXYepWAtd1m2Ddpm+wec7k2mdnERAUXCI8+fFWfnAa7Opjp1+jRqwk4JVgRvsojlf78nbKl7To8PyTTzDr4oth+ybCTT46zpwPwOKpH3VpX2NAZ86L2xkd62PMAzMQD4tmMjpZchQCM6dk8+7CjRzoEIBMnmnN5BN7PvfqH5Flq2dh69m0hGFNxV5mh1+lUguz9KlKvpWLuc3nJuLuwJv6d55IfA2/zQMCRHQb8/XBrFQHIRkGU+3bGBHYi+PNY6mMmUQgbSJrdpdSIlhh+x7BJBYBl+PXR2kdxf8uHCUpvxN+ygIbLRxDoGQDA15sIM31EcZVA9lXsISWVzvFsU0BszURyeag1ruPtI48DoTHcSBs6QB4Y7uKp7QE4yi9/xQAdqxYxPIPDu3b1/dsIsGvyYyO55zXu8+oWlVygI/vugkp9bd5w0dMk7jObLZta/cgOSXsqTGYmo6paZi6Cm1rkJOzCDbrVAz51jpQl6xZIOA2h3Jsyq1QA/pXTejAsUwmwSezNm0tztRERvWbSKThTd5p/4rS2zfTrz4JQZQRJQVRsr4rxVRsrpQe22oYJkmC5UyafswsWncsQXLFIHliUDyxyM6oIxKchUJWyKZs+3X+DAAt2iGi1GgTeHV7BU//CpIiijJKOIGIoxpD0xAPW7LIyphNZe08HFIK0epaUqN7lgnXDIOecuRtWbUWpXAFAOve/xK704mo2LA5HCh2G4rDjs1uI7OgkfJCO9U7LZVoPeLg82fe58J7rqHeV8azGx7AZ5i8N/VJ4jWFen8Ni4o/533//oPnahdFIoFmlKhDbe3bN8LmPfDxy21c/0rPfdHqjyABQTWbyqwM7KFmwtpCnj5LxBQFbhz0GFePPrXLMbHRXm684EQ2r1vG2V/6iBasCUGSEsQjhvH5TMtnw7D8NnRTQDMFNER0ZNpkF/MnnmVV5vsZ8TgMoqHzxYB0xqalENE0gqpGQNMIqBo+VeO/O2r4SrIhdfo+HbIzHrqfqm6iS076ZPYuEOfsM4Dm+EM6Lx3KAeuaHv4nHruD2vhEVgwfRVqDgRbsmmVeV0sJt7QQFG2IoswKWWNnlMzJ1SpT6jWO9Xa2Rx0MKmRGkvnYvYQDai0v/ve/nLZtLk2CnSjJhoROTMseWmMLiG/cgddXjiHaaE4diz0cTekjX5L74KGw8zWrLWG/LEXixVtuJ691OemniNQ2XMdAI5qwDJfn/JNxbbsZrdUBGgEhzJuOFUxS+3O2nE62IVKBgc3vZb/vDC5at5iYgIPh0lmElSJGi8Vkf1WF7hA49eSnAHjS8zHnt30DbSuIJZaNAx/HMAw27dpNEIVBA4f02t9H8b8XR0nK7wZrbThn6PUEHRV08CAHEhSk0iAJBTD40mLe2H4FsqYSTS6DqyCt48iQ4Pq047gocR8fbLVMwP3irNDcprDKhwcWkMEhr3dHqJlWSULRenZwrSy3Xiq3+kVqF21CNg1kw0AxDWTTxIbBX1JjufJnOWxUQcDWSVKMsEncjDHEP/RWt+cIN+6A7TAs/i/ED70VTfOz6dtLAAHvFX2JhEPogTBaUKXxxxLE/uncd/Mhaf93jFe4bO21+OI9xNsHoKsqhqaiaxF0TSUixeKUevbib+0I8lPMQMK8I60Ihtkpn2+K+BwiYVEiUbWcIO8saeBA41xETEQ1QB/DJFYQkTGRAFkwkRGQRCgTo0BKIV4L0SQ7SHXZ8QVVPL/CR0W1W0sagcZyPCmHEnIe2/826H8bZWWr2Ld/LUovy0+6YXYbZg2w+LPF1h+Cjb3r5oOpQm/WJ8EJGCQMSqQ0dT9D3jmUbynVbmN4H8uqlQmMGnoJl9Vt585PrmZztOUM/vdvZvGfiy1S1F5SyuY9lnhdalQ1vcEM6oDA1CsyKHn9DcyCDE67+zvMwmreK6vjmyaJ73/YQYwic/+YHLI81n2vKNnLJV82AE7+NjzCheff/8vhvaYJeoSyqkJeOwDvJ3QwOm8MIVUlouuEVY2IpmEIAqIokhHlIbrTydsmy9hkmejDqkvZ3QK6r/tzdULTDeRfaFdHROOrkI9F7jD5iMTaduNIXESSp45Pss9h+ZBj2d4mEdSb6F/9KpMCSZw6/TqSAzba6mvoO/NyJEWhbl8zzxFr6TYCJ9dpTKq3CJiW6MQ2MY2i3ZtoLAnR158DiCiozJt5LAUc4DNjOt84JxOZlMzXS9tIIZ5A5cfM6xMPvh3sZzBjlBnU3f0StkQXDVlZLNxo1d/UWsv5m6wJ1evP6Sy4pJHzOoZCGD4MvsbGmrEEJAMZiFIF2hWTlUoRK5UiLgtNZW+MySZ5N/lYz3u+kkGG3gfZGE9H7QYApNAhEvc332xOcy3AaUTwD7+SGWfNxjRN6v/7KmU1taQdJSl/WBwlKb8TzM48IoIokTH+AvZfvZyadRXotRHk1mRiYupIGtvMt5zJHW++xtRN62iJ7U/l9L58Xz+ECklDMTXGpafyyNmX88jsrvWX+0JUmQfYOOg/nLnrZgBS6tZTmSEiqz2TFFecNXyPsYkkOgxCukHIMAgZJmHDIKV2EUmFWyld5wLZjiDZQbYz1LaX+AONBF6vw50SROxF9EoLWQOwZItBEAQUxYMQtqHZWonu19UyUL68GOFnTrwjCybiWOWiT85oLp59xxH1f37fIry9CKw1tbTzwZ7x5J94CmOG5qEHWtGD7RihdsyQDzPs48C2VYiBAI6J2UhamGZd5QCJqDEuElHRgepgEyFTQZaj0RAJA7phKXlqhkCAMMPUIo632XhazuVfBKhZXszTJ/WepLCheDkAnuBwXEnZ3Zap6RQXKym5iY07c3mv+DQiaiqKJGITBRRZZFODSJbzyAGwsroDKTgWY/Q0br1jPKZpoqkGGAZBf4BIMEw4ECESDBMJdn6HwrQHI2yQ36TAUQI11v3NjR3ExMyT2NpcwfC4TFpCLby+43XyYvKYMvIjBtVv4Kv2h1mnNvPcF7NQJAVhdyp0LtWdfPO0I9oH0NQa5J3XtiMDQdGk/8gCBv33Xwf3v1hSR4ViougaugiGqTF3QxGOgIYoiZjrazFx8up0kZNOvLDbcxwBQQDZTlC0A2Fi7DZinA5w/r+Frfr9EUSbiWEYHNi8h2UffEZ7XRGyPZozbrudmn37qWzQuQKF759aa+nhCmB2khZTtJZ6dte2sxKVErvB3gFR6OlT6RfIQU3azNeJ08mnCUOyM3VLHX97Xwdq4L/38cTJIqOnjyG0U2LAsCsOtivHb5DtM+jfquMjRKvoJ7bBjfxVEIMWShz1gMgD/Ns6oPPnN4ullARS2BSMJ04TkWL6EDjuDsx9byIApb6dxNpSyI8ehVoPrnq4BIk6XxEDAyohbzqODiuUv9TYAkxHw+Sl/ecBsMtIZbqrBtOsY6i+jx2SNSlrENvIil5NfPY66vePpG9TIa5gHET14R3/LE5baTkj63ExRIbE4i2pJZwVw0uBc7jx8qfItnlpaGjgu+++o6ymllEjR5BV8PM0jkfxR8FRkvK7oZOkYC0r9L31Zfp/eDKtlXWki7fg7/icVG81YcHJxG2bEE2D+ObdxH+2m2F8e7CW8vHd50IaEe/FrOzgkc8qcUVuJmKLwhFqQhNz8OTl9Niqtk6nwdlD+jO1IP+I/Y2PXIxkGJh+G6KpIXV+UiQNJVVDrNyDayKEE3oONdYjlvOgbD+kJpmcPYP9wYfw1ZfiOWxgtoedBB1d09QbpkFYDBLtiKY7BHSdFHfPTo9Nra1EDBln7mjiRnWfEO3rwJ20r9jB3Rd9c3DbSODcw8qMfmswl6dM5qIZL3dbx4G9q8j9YCaFlyyi5YCNN+UI5XrvIdgArdWbABg6/qUeNVBysicyf8EgEhJcVAViKGzJYkBUAEOX8KsmWhDSbDA5J+aIYz9/ZxeaABf/2XI4FQQBxSYBEkoPfTp/dx03l1ejKBdwodyXO4Y6ebN0O/tbtnCgZRfvbX8GUY7H0JoOHjNIEwlF9ScoiGiCydzWXWiYnF1mZXG+6omR2KKPbB/AmvU12Pf7abcJpE9M5q3viikVdOwpTmKdCi2mQYoqsnWm5ZOzpqqVi3YcIGyX0CUBJqdxf/E8TjrxHisDtKqzY+1C4mK89B06qefON3SqC1eCMga73PMzVN5Rw993rkFHRBFEJFE8mAE5SnHyyOAJVO0/gFiQwL8vsLK+i5KbuMyhNJau4dOHrHxG52dczlglgTqfimB2BkN1fguAYMIs7CTEOJl9+zhW1xfxQlk5l2bZGZ96A5nlW2gtt7HGnoE7uK1LG69eYHD+iE3QsokvE4YzrbyFAQ31DBRMEpMTqUsLsr5mB7pgIJiQYcRTIVn3b/roCfg3voybUJc6680YXhKupFq8mTbRpGDBe0yONLJq4AgQJHYENVrEIJ6oOoINRVT69zLC1LG5z8A+5R9UbHuE4sxsFmetZZP2N8Y1XI+hJiJEdHyawre+LJLEKCShkrT4E4kqaWVDxEb/4z8jDhjQns2V6ZWY5ms8ygDe3jGL0zrb9uyZZ5OY2sozqdbEpc5MYOHKsezfcAatYScxMTFccskl9O17pHPyUfxxcJSk/E4IBa1Q2i0bHsTOeBTFiymX44xXcHmzSIq5j2xZ4uX6dhzqYREx07NIvvJWKv7+T2wlLcQk9zwrnxmchiPyAYKp4Qw1UTp4KLrkx+HtfiACaPVbhCCpB7E0j+5n89i7mHDSbUfsM00Ts2wdwtsnYc/t+UWghTolzB0JtLWVUVz8LT+2b+db5THc6yowQw20FLci+DQaTLhUlzg8n3FHyIcpmEQ7uw8lDRoG3m6k0X9CS6vlXxIT07MgXCQYQFd6NsMbukZYFHDIPS+3aMFWqz24eVsKAwIrnCZjFmwhVoOIAGmI3D+sDwUZMYfqNiNIqgdnXHKPdScnZ/KnyywC5V/9PezQeOcvx5IU0/MxAJXl7YhlfoRB0aQk/PpIpT/X1YBdYF7mKEbmnQjApSOgKdhMYXsdn5csp7i1jOh1C0luDuBzQqhPiHZfBZqoYyLTHNWfIRxjtdne2iNBaapvYcOmSpKAr1xh6raUYgLhk9IhoEIAcEn0NQ5Zy8anx3AgfSQA76/fzO1+kQ/SB3HcQ6NI1ypxEmZMZzZI7QuRMDbC2IhgIyJ0frARZ7bi8SbBiDFEjK7PUFiL8MbeJXRoKrt9YRaG+jJILMXAQDcNdDT8pp1avLyzbAeZg13IEQ3ZHkvfUcdzwtWzsTvtfPnkfzmw6Ruik4cwNMpECzYz+Z9n9tj3K59cS3y8DbssMS1tENPSDv3m8+qjuMTmxBkKMiVVJPjEbbTXVJD8nzkA3PmpzhPnSYRbKygo2Y4B7ARo348ADE7sx6QTp7Dls+VsC5cerHfJhpWsqzye2HAtJ2UUk+lo51HlXP52zL+JtnegKR/ydHKYe+z1ZHVA6ohq6vdfhyzEsDGunV3ZAiPsMrFeN4k+F8mOnayM/palo05jk7AcJSTSFE7m/bFdowhtq+t43f5vcoJVTMs/l1Pi3sW+9VI63jgTKbqYOG8U4ejzibSP5CYgaZLBM7ZZvGb/N5OjtpC4fye+bPBIEPviMDx1LbRNDjFq7GhOPPV0FOWoTP4fHUdJyu+EjhZrrd6Ui/CHqxGEAM4EjfYKDwv/+xD9okYxMv54bsn42bLF0mrqVtzNTz+1P7VFCM7bZPlDmCAjoAhgQ+CE9iC5dhdjt2xA13Uya6rZfdt12KN6Hpzb/Fa7Er1HOoiqahiHEUF0dE8OBEGAQGcCOG/PjqslDVYo8cqd1yLJlibCi0WvIdUEMcV2BKMdU4ArMuJ5s6IJX3xXstDUZjm9xrq6J1shw8Tbi85LW7vlJxAf2zNZU4NBDFvPgnbhsKWcaVd6djDWA21WO2MTGF5UzeZOThBtCOgSVMgmRbLJ4r2lsBe8mskUXeZPpokg/PqfYli1fEkcv+IF/MWHu/EmFjNs7CB0XUeSeg8b/zmG5XTNpRLvjGOSM45JyZb5vKg4DfOjF1HcGn1PqWfvpe8jJ2Xz4JZ3CW0NMqrQst7kjI5m18YDKIqEpEgoioxik5BtMnOeWkNSwIGKSYbLTrYkEpfo5CcB/dcz0rDbZcamHHoO28PtSKIDt2Jju1wFZKKIIdq8/WiKm4nDG4thGPjDOgImoh5C1MIIegjJiCDqISQjTL0e5oNYi4S909LInn1LsYkSNlFir6+FpxrSsRFGQqePUMP8Sadgk7r2+0s7PuPBxjwqxHhylH389d2uTupn/e1qwIra2vnQV5i/kPgwoOnsD0bou2QVZmco809HBDt9NN76960kl7cCcPhCa0GlVfLTrc8Ch5bWRogip9x1N+G2FtbP+ZQB00cxMuNYls+dy86aGkxBxJ+Rh94SxecNE+jf72uydCd7SgeyLX4T5/Vppxk/t18lI2FyY3uENeFiNk+aRKBPOtet/5xxx37a5Tr6AtF7t1Ik24k2Y7grtYUN5jO8INwKgKCqvJ57Aw3JBjP5Bk1QeDXzLm6fey9VsbeAcQqTlesp8N/Kjs4612+7mDcTvdAOqe2LeSo5kW+rXTwxR6Mkczb2qGaGJuzhlLPO6bWPj+KPg6Mk5XdCW1MuRXNew8SgI6kOwWaiiBqCKjBkWDwD26Mp8+3Gs2k1B1LSSG9pJWrA2aBrSFESFYrGmxnRpLhikCSJsGEQwSQCqKZJCANRUAl1rqdLkkSw0cqM7OhhBgvga24kotiI8xw5+PoDrcQAcg8kBYAGK4tpbyRF8A2hLVKK056BxzOR3L4nIf1QAkCUUyS/IInBqVFcOTSRNx9bRZynq0xcU3snSfF0fx0hTLy9OKf62jstKVE9R+r4OlqJafHx47tPMvKcvxLr7tqGULgVAGcvjqtGuA0DAY87mrkzE9ENE1nqSnwWFdbyTVUzc6QIHbLAd7LO/tAI7uPzHuv9OSK6DojYld51PfYVVtMn8zE8qTtp8cHSZSAGTmHUhHvwxib36lj6akIy1zTWMXb+aiZH25mYGMcZ/bJRfkZy+t90A8GzJqB9cjE7Z37FkLyxALw5+TYe+eGrg+XqVwnUryrt4WwOIu56OpwH+OTuO5EVCcM0KVq4g0LF4MrKapKEZnz7nUQJIZpND5FO2p4qNFBjZuI2O7jR8Tjxsz8lN6W7HFQ9w7ZuMQRgji+WOV38Xl0IpsH6sbmkuI/MjP0T/jLkXK7UVZYsvBwttIWui4Rdoag23HipWbqTuJE5aFoY0zARJRlJkhBlmTh8JIomf46zLKo/3ScBCH73NSOW7TpIUEr/MpPsl+YBEJEh7/uvcc07m8+FJuZ8/AnNV53Ewo4YdgcDVNz7Dxo7w8WXLVhwZOMEgVBcMrZAMbu2ZCE5Uoh1no3aT+DmIRfxsP9uDEcH2TYDQahkQPQ2Wjz92SZnk9dv7cFqGgJxRBerJG0LkLrFzhMTZtJnVzFaczG2hyqZ2ezlgX2nEV21GenP1jL4v7iRm3mFIfs2Igh2BFNHFAx8eiyl+c+TWH8iGbE6VEOM02Q1DlIPiJAMSSEH40fsIzdyFz8ygqmX/rP3G34UfygcJSm/E/z2VgBaEquw2WXMsIiq2vG2JONySmCHWFsy45VTkUYGSYg8QyCjP0prEq6xKWj5XubV17I8P4f89O4tAp8v/Rz9MIn5UJO1zGKP7TmBX9DnI+xwIXXjCxFsLicGcBYuZI8Uj+LwkFUwEvnwwbGjM/wxqWcxON1nZ2fhCdx7/4MHX7gHHh1A7j3zOC9H5r7zRwGwqTOVfeLPxN2aOiySEt8NSWntCDFIrmbbvnIeeW0vimLDZlOw2204bDacdhs129eTHNFpWrsWf0ICtpgYlKgolOhoFJsd0zQ5butekg5EmJWXS/tDiwDQ0l2MjHdzdbmGS23h9OTn0VtX93idZrCNdtmNS5atrMndKKsdPyCF4wekcF9rEBP409piNjmSudR8kcWt5fSLyTqy4p/Ba1cAnYL7ljHv+nwGZvbrttyGfa+RlLoTXXWAaUOytWO45rJh61xMQ0QLppAQdTkDRp6B3eFFPswyc9qgZL557mu2ZebwRdDGRzUdvLB/Nbf268d984torfHjiLUT7bXjcsh8fN0WhnQSu5Cm8cqy1ejKdhqTJE47ZzZe2UYkpKGrOppmoKk6uqajqQaGZrBpdQWGHMEwDUBCFAR+PH4w582byyp3JoahcUlMJW2aysd+Sydltnsvc/2pOAhylbKaaLWdjUWfk5ty5y/2YZd7IlVyvHktBcMW43I6CesqqqES1lXcirNXgvITbJKCTQ+iab2/Ut0Ry6qpL2ihYUFLt2VScCFLRVw47Koj9mn5U6lMfpQ3dh5AnjGM+098mqU2D437dnLyvf/FEx3Pyku30NpRR+PjJ2BExzMjIYuS/QcAkxhNY5/ctY2DwmH8CJTabWDoSP5W4qK9BM1YVEB2XIehOCktOoWJ/Rcg2JsBmJSxnhGNbn488GfiFkcRusiFwxEg0dXMzrX9yN5eRsvZD1IuVVGe10D/omhS7jGgT4ATc2ycmT4ZY+E4Mu1nsWhTGqcf+wgzFhQR8AxlUHIrKHt4zfw7s9oUTtJLaM99mu9KJhGODVGf2kHUu+08V+ikMDmG752D8Q49jWsevhn5f6gcfBT/u3CUpPxOcKbBK+P/ypenf0le7KGZXlHJflYt3UrGnngkrwujw0e0LYddxn/Ib7Vmra27KwjkWLoVLnvPt8zRUI8tEqatqQaHy0uw2SIpzvj4Ho8JB4Potu6jGdobK0gFBpV+CqWWKbeOeEoyzgSbG8HmIrVmO+mmgBTVs+5DKBjAjtZl5q52SpHblUPXU9NhLZck/WzpqcVvbY+POZJstTR3MFKpwmfaaK9uRTQNJFNHFg5FNOXUlnNMYSn+m2/Ff9ixJqCLArJhYvdGs3nEKK7cO5/nc04jxajju8Y7mR+axuODL2OfM4urD6jEbk5iXdFzrE5eTsQWQhQkJMFyogy1VJDsjeLSHpxfD0dCjEUmXx7Vl3/t/pY5xgDqgy2/iqQkeD2A1Sc1rfXdkpRWXyNe+6dURU7h0pOfs6TfTZPt6z6juW0O2LeiuKtp0x9m7QYr47Hqy8HrnMKgkVdQUeIjs7WWa0ZOZOSEAby9vZCHNoa4acWmg+cItYQRBIG68naWVjQzq7/1DDzyyedIe628UhNnncvIQd2TqMOxfdtO0OGZx5/nrvus5QBRFHl4qJfn9n3KqXzFiIT3Scsawa0d1bRHfAyOP49/GSZBXeW7FcvQDIkZ467p8Rzb9ldTVNlAiy+Eyy4zrE8iQ/pmoGut2NGJd8Xh/QV1WoBNpXMoOvAMhuBGNFVSqMLemked3IhPd3PVGw8we/gopo045aAjdGtjA6t/XESbv4g4zcWgi0+mY1ctthg3giRimgamrmPqBrtXfo+YGGB4N+eWHV6yr3iMH9//jjPsVlunXtvVcqBICrJpvTsERWHsZZcxFsuHrL1oJTWbP6Qxsg6b6CLKNhhbSjqibMM393P0ZW0MWVV4sN2GrrPh9ef5x75Crgl9ynMfj2P4NRZJidR6GFA2kUFrvqRd3kOz45CTuBhlMG9YX1TXNkIa0CeLij5ZyKrKoIp9jOvzL2i7G9FU+Dp1HFtHjee0tevIq9IoSannlpR6pgQTSap3MkmTiJcH8tfgR1yz/S5K7FZabTPLQB9cQ4PRl1ZniLtuvA3xF1Swj+KPh6Mk5XeCZlg/YOVn69n9c/qSl9WH2r+voWK0xrAhx9H+ynbyHYd+bLYOhYbGJpDA3YvvRe7aVQBUT7Qyje5IT4CEaN5/8p+E7AamJKBEBGzIiJKlwClFItg83S/nbI+ewEWhl7nr5AxGJTnxzb2XqEAF2ZVf4yCEywxhEzR2mDkM6WVgbg+Ej1ha0PQjSUpjZ8LFn5OU1k5fj/iomCPqNoOWj8us409lzORDsti6btARDNHhD1H84P2E7XYSHn0IrcOH5utA9/kw/H6EQAA+/wqBGFqjL0a27+LastcB+Dp0Dm5y+ffHzxMlteKe/h+gAPzQoGxht3cvOp0CYZjUyAKpzjRObPRbFga7/ItaHVmJHi4oGMycQh1R+HVJHtfvL0UWvJw2sJlj8o7ttszC9U/hwmDqqL8BHBx0hk+YBcwCwDB0Cjd/S217p1O0aRCS3mbTtrep3vMkoiEzcvwAZFkmXbchNEWwSyYPXDSSWf1TEEWRj3ZXc/e7W9hZ18Gs/ql8+M23SHsLaU9M5em/XP3LWiWd+Ms9F/PQQw8SMtoxDONgewdkTeX6+jrq2j+mcN+5pGXtJ8t7KGxdFgW8og3dMJBFnc+X3c/FJ/67yzKbqunc8c5ivtprOTMfQi0i27hl1HwGxsOKFaOp1yYwbugd9M8o6LadlS07aT1wN7JuR6aVGMnS0wlE7UU2RHZqftbKn7N25+cMWPYaI/WhZMdkUFxWgSErEC+gtJQzdXQBGROHdXuO75c9SjIxvfZXhw7vl66nf9pXhKRUBOGnZSFLkN8s2U/eQAOxxSIUZSufprLyfUJJ7ZAMjqYogo5mOrwLLbauAidCbLqjS4SZKEm8fc3N1FbsY/vaXChawdZO4cmzL7oRpc1F+6nJOF9cQtpfAuiiyQ/Dc2nO83LFNQ/zwqK38JQo3HTdDaycu5ot5VvYm3YVaW1hoqQr+WxMP2r3TAeCvDHrDKL2R4hPL+cfGc/w9q7ZRDfksNIHMjo1gkaLzY63qpx1jsFsnjyXkb7JIIRQEWhtbSAurudl56P4Y+IoSfmdoBrWy0wWj+zycNgK+xNtElHZ0bjuGkPt4xu6lAns9sMQ50Ep+i21beTEuHhnRxUhzeDSoem8duls4qrLmXJMP/RQCHdLK/a9VYijc1F0DTUSwlxbihBlI8oTh66pBBqbECICz763HVEWkX/62EQ2HGgmNhJLWWUM0UI0zmPfIOJUcDllFI9OlNfBm+99zNwKGyt6ufYN9VabH7/zdhymgIyAhgziRF5c18KuNa9jFwUqDBOkVA78+17a3U5aEoO0jE3ly5otIMDerV8jKS4UxYGsuFBsTmqrLJO529N1BixJIjEeFzEeF9WmjuZ0knHKaUe0DcB48CHmXPAqAGZ4EPA9AK3x4+mw5RJ0JTFqr+UM2TTZQ/wKHycPnc2jU7uK1935ypvcWNoP4+kttAC1IvgVgaAiErQJhOwSEZtIfaicyrbtmIoTcvoSEFVGBQW21RVRa19AS4tGTIxFcLp8RAEBk317A8RG+4jLHMyrmxbgkB04ZRtumwOn7MCl2KlvX4pLmUpWZ96T7iCKEgNHnYGxXiO7YAouTxwXvfgQ9YEEjguXkOBNR1Zk1u+r5brP9wEmC/4yhpz0QxFFtR0WSdxRYzkW1zc1MZl1pDbUU/rMpwh2L4nn/gt3Su9hoJIkkpVQQGNgLZ+9+W+GjCwAQUBARJYOkeiNm2YxYvi7SFJXn6GzJt3A1yt9ZNo/4YGP0ohzTcIuC7QGNRYUtVAZlDg9V+HqE4eRGO2m1R9mfXElB2pbqQtciCPyMU2hWPqnb6a06HTW7TiJ3KzTyM8YTXJ0LJtr1rBrz/2kGZaybv/+TzA041Sef2UQr7lEfiI/2bap5DkFLkgbzu6SckJAka8ar03hvIsu5ru3XqM+Op5/P/4YF132JzKzj5QHUDWFfU06F7/0FhnOBu49/0oEQcblOnIy8Y/V93bbn+kdBnfcoAGfs+6tlfj61GHXnGS0nUrW1NtwxloWO9MwiFRVYmKweetUXDE9LEFl5pGSeSejzruTyp3f87cv7mdh7ZO05LcTEiP0uSqav5ZMYfCQybRueISk0gb2fPkm3kbrPj3/8ouAgaibXIgdsPNJ88t81uRm2zF2HJEwIZsdEuHhkjMAmY11wwj0MRldUc1o81l2byhheMFrhLExIPI9p+lTGakOYYmyizAq8xf8g/PPexpF6dlB/ij+eDhKUn4n7K/cznkrdDbsu5O9UcmILheyy02TFEVZdDq5RGEUK+zNacTpUnDdPgqHQ8Jml6i9dw2Ndmt2owsm2ffNh0hXgbaXvy6ESZOJH1vPP46b3V0TqGmq4MO115F52jTOOf06AB6+4iacEZHQlmZE3UQ0QDJMFB0GAYOww8pGimnsUleTaPBmVBhIQ3JUMOGtQdhNK8rIZqUWxCaI2ASJcHwiGa15FJQ2omamorijUDWD0Xotpi7RaEiENZGAYTA4WEja9l2ktZpkiHBpbCGaKJBl06n3333ENbW0pgAn4Pb2HDFhdvjQexHoEkSJITtfZemx/0EQVYZeXsSeL15BEEQEwUfMxD54rnsW5vnwSJZFzOE6Urxuks1Ni9yB7/ShhP0R1ICKFtQgqCOFdWxhHXdQZ1vbTitBsBqE4p24gTFALVDbuSBVW9vSmaxNwDSxojxMAdMU6PDsIxRbyJyy73q8JoDzk9Re9wNUNNWxoj2GwsJCXl+xh+LmMUQJIVT7DtL7WqkY7LJl1RMFg3u+mkd+kp2B8TZGZQ3ik83WczEtz/LbuOmyS/E9fA+a5EZTTZI6NlDy8U2oqSMRZTuizYlocyHZnUg2F5LDQ/ygqch2F+OPk6musa6purX79ra1bWTvvsfpX3D/wW21tXspLHyf6PBqcEJFaxvv77QqEDBJVAyenpnF2cceynuUHOulIONQ3pw7H6gkOiOPE6a+xNw1zxKnzyFSN5fttQKNJBBPAy5doClqMmP6XU9O/BgAZk9+gNc2PWRVojn49rLnD9b5wKIHAHhl6tkAhFUH9zz8BJuXL2Hu9wt58823OHb8WKbNOKXrNYaiKGrMZ62RRJJT4IR11rlE8WKmHHs/oiiy7ZItnLHkZbapGbw0MMtKDmialNa3sn/ecqLpAD4DIBTdRGrLdPrPfglR7mrJFUQRe6ZFWBwbDPwxXkxdt/Jv9YCnFn5Ea3wcBYKdbOlYXPHRvCC8wZb6BaQ9Pp8LOsu5Kj5m1+TZdIgWUZEddZz02SY6fJ9RmzqRSMGFtDj9gJ2Qzc67m29hXsUg2mqGML3/Cs5P3sWr+adx3PpnGTamFmdYxUuQeFsbJ7ccgy9YzkfyHuaHhjPFF2b44MXs2/c4AwY81mPbj+KPh6Mk5XeCsmMf5600AWtN38BKHvfPY29kT5wMBKAKeHPdwWNsiT9gj1+CvUChJuMZwE6/t1YhHyQoJk/JrxIj+PhWH8+n5uk0+at4Y8cbXDHkCn6OkkrLTyDKe8i3wxUKgxnhlscmYLMfcjhTdYNARMPnixAKaAQCKsGgTiCosem77Th8BpcfJxBsLCG+fQXxSRMI6WFUPUJEjxAxIkQMKy28Ghsk193G8A+2wQ0XMmCyJXZ1pED9T7id/Y9di/7VUjZdshUTgUgogBYOEokEUMNBVNWPGg4S8i0HVOKSe4lA8vsxe/E10FvbEA2VYxxlrA/1oeSHR7DHPkx7SRSgIzpz2LJAYSRnYy5vA+x4oo6crTl1CCphxh/Ts/UCIPYJEbEthHxhJt99PY92tR5MAcEUMUUrvPiGv9xLVA+6Ltd/ej3VHdV8ft5c/JEQfjVEIBLG1/ntV4M8suV6kmJ+OR/TXXPmsbo8GQgjCclcMtLPnt0+NFNk8nHWoO7Rd/D4pAdYUnEs1cH+fLHDxTsRJ3DgYD3nF1jWFVEUcZhhNo65nbFT/0L7k4PIal2N1Gpl3v3B5WReRwyz12tkiRESR7chfgOVqeMJRpdAEoiczpCBNx7Md4VpIogS0fGJLF8xjLq6r+hfcD96OMS6edcRiLLseILYD1W8jttPmc3LqUkEVR2PXcZu++VQbQErOaPbEcX50+5lT/NfqanbwWs1a0imllDzd6z3y2w58e0uxyUNPp/1eSexvGIv9p85pIbiQ0iGRHzYoMku0hi0onVGHjuN7Pz+vPPKSyxbt4Hd27Zy/qWXkZhmPTeSIJASbf0W851thELZiKKIzfY+CxZsZsjQ+9mi+tggTubmtHJOzRp18Jzv7lyIS9UQbFbfCVH3M2V6119aq7+Z9WvHUhscxOnT3ibGHQNAQtwU9hqr2bxgKJ7Y22gW+zN+3ISDx6nbN7Hh+1XE+rM5hmzuu/de9u1YzifLX4JoiG9xM29YMontfkaX1FJuj8ZRdzzTV9yMYhrsmJTKRVP/zlff3UNKzSpSalYxfSk0RseyITuBvll3kVljkbyiDC//rPoP/tVhQumXs2H0fVwhW6KW//ikAUGQCNatY32/KYyqkHEGV/HBihs5t/ZJcrJvx+Hs2Q/vKP5YOEpSfifMyDyBKqxZYvQX7xGdNwA16KPj/oWcKtcxe9ZEnBEvgaBGIKQSiug8UvEhpp7KNcmnUxTaxyJXHkpoLw2kktNRzUkZHzM/tpwNTgd/a3qX1Vou7eoO3ttd2i1JWbPMEgPrk9n/4DYBAUNv4IvHXmT2A4fUbBVJJNppI9p5pKf8xwtfZ0jLsYxwOTjhglOAG37x+re+/i9gI6Iv+Kv6S+9oAYeE2Lk85nR5wXXkoH2geidQi9t9ZDbZgwj4MXsQqwOIVNZa33n/pl+eiagE2PdNNmAQleqi8IdS0l39INlavq901TMmf8IR9QiqiSb1no3ZujjL5yJ/cDa3Dv4LkZCGKAlIssjc91exf20rNpu9x8PDehhFUEj2RgNHkiV/xM8jW8DdmRyxXdNZ1dLBxFgvUbLEvpoS9tWW47DZ2Nsgc3xuGTeeMIHclH54nW7ueOQ/qEoicdEWyWlpKSbR1cz9Z1xOWtogTNPk67nD0VrH8WbZsew0EvjXqq+ZmppCSzjEpaaGrLiRbA6i/tGZeNAwwFB5e/6l3P737TgjCm0oxAwI4YiKkFGzBldAoSZqJJMn3UlU1JG+BcGglRpA09rZuu1KmpqWQJTAgQMjOePER0ju09VB1/Wb1O1NRFHENE1uX7OPD8J+wA3C8dgiBnGh1UhK9/fE6YjmpH6jj9huLc6ZrE9MJ1LWTuy0QzmZ4lJSuene+/nuww/YuncfL738CrPOOYf+w0cgmAaZCTEk+VV2t2UyZdo8PE47mze/QUPjMxQVzWadcSYu4SIuz+uqpjs6P48Da1YhdDqOh+1HEtVg2I8kGKS7dvDtklnMOPZ9EqISSZ74OsUrbqPVOZ+NtR/w2PpbuXjNS5x+zACGuBJpmVONaotnWeqX2HQblzxzPlGKEyN9J0mRDBaPmcTw3Ruoi3Kxpk86qa0ySc5N/OmUxzmj+gOO2XsAfVKI6Fka0q4wREyqhmazQx5GQV06dsnFedl3oJgXsbuwDwOidjIh3EaNGkfdV4O5t/1RtmSeRtaeDwkMvAOX7RTaBZHtcguX+7eTE+lACF7Agc1rGTjxlCOu+yj+mDhKUn4vdEaz5G/cgOTpdAy1uQmJCulOk0lDjnTUe+ItDbuUzpWnXn9w29yNc8j96mlWDxD4zzESYL2JP47ysP6MPzHpkzexKd3rgegRFdUF+XmHHPb+/MyjvHHTpejqLy8N/ITUQW6ohd0bKjhh4q87Rup0Cm3YvIaCGbN+sbze2o5U13POoZ8QCPgBoVev/ujSMgxRpHrtWhSvF5vHg83rxeb1IikKodpOi4BHYeygdzCMCJMezMIRnYCmqvzw9h1E+V1Qa3kedOQZSN2dTwVN/uU2o5sYhyUCtB3mDK3YJGTdhST17EQbNsLYhJ7DLIOq5R/SZEaTsmRrl315LjvSkg2Utf4004xnYJqdYTnDASipacIVaSVm8BT8mo5bltB1y2cqMbFzkDUMvC4f/eMdnHvxRby7+hvuC2TzQbOdoR21XAocs+xvMOR4SOj0RRFFKvz11O3fhbNTUNmW1xfHQxsR3W7aXh2AZMIpM3/o8bp8vr0H/25qsgQCQ3UTqK7K5dW3PiLJFosiSthkGzPOOZWEnF/nRLl9+6f0y9xMiZHGcT9sZ3enFWIKNuIVmQfHZnP69/lIwm9TLxUQMAUT96hk3KOOVAaWJJkzLrmMCTU1vPrSiyz49puDJEUURU4dEMOb2/zsKKll/MA+jBx5BeHwbNauvZ/r5p/BdfhYXLyHpP5JTD/O6ueBOTncctfd/Ps7y4fK0Y2z+e4Sq4/LQjNIUJazZNVpBMwCYqRteJQO2iPRxNuGcGPfBj4NDGR9nZeHdrSSRYRxp+Zx1pxxuAr2kjNgC/v3jSZJ8XJtwlPsSoNhuzcgIvDEsL8QEe38c83rePrMI7rcQ2F6DE+F3+I114U8MPg/AKSwnQL2EY4/gY6QnTciKlc5PyTNFqIyZHCi/Crle69GaJQ4MKgvz0ccDE4QOder8GHc11yUuJ33nU7k7PvIrghRUfE2O1d5Gfgr30tH8b8fR0nK7wRTs2bYws+iYIKigsve/cCmmmG84iGzZeueXeS+ZWWz9f1slnjiiOsQRRlN17A7uh/A9FAEw971/DHJcYhyDNJv0Ba4/7w7eHzNp3hsvy5yAyD11LNpeOo1xJxfDrEFIMqB+SuCXYJByzKjaWFkuWfrg2gYtP3pz0ds10URUxSQgfjBVxOd2VXvRVYUZl71H8pemA+AYigoru7PI6qg/wqSIhgm9JCtWNd/SkTZc99GjAhKLwOmP2I5P+5TLStL7bIpAGz0DuTUkS/jisRzcn4t100fS0RVGZp9SHZ93spNlMUl8WpULE+s2EHttOFoehDT5KCzqtFJgkTJWkK7dMLpnB8JEeqoxxUeTgPXkbj5Zfjsz3Dt8oN1z/xiJnNeOWRpyv3664O+D4IawXD0LLYHkJg4nYz0y6isegcAT2AYfUfex4bi9ymI6gOmSVhX2e+rpHDdDib/CpLy4493gPAF2TmwK5BDoTmUXL/A48OzOTbj5yHvvSvF/hwCwqElq96uKzUVXZIJRFR8HR1gGqzY38zmJj/5XpWx/TMPlt1c08zlofP4xKGSEjKZVKFBRTXGtJyDUTnRDjsXT59M2dbHEIVDZNoX9PHjlg/whJ5EN0XOnPwQ1U2lbNl5H7JQTas5nYSUU5g+YDqCIPDyjkpKGy2fo4Wxdfx58gDsIwaSuKUYW5yV4bhv3kZME15v/web5yYBAh0DRnGOuYuPwiNZmy/R7m3igxOaUWWTW+Y5GWhsY1mfC0jy7KWAzVTaIqS7v8OjbOf2yFged+dij+wiwfByZuRWvpy4lGHFpbjMKq6qeAddFPivcyMnCnMp+rQPp+al8thpDhjk4LEPC2hLP+R7dBR/fBwlKb8XjM6X82Hr1oZhEJIU3I7uBzadEHbpkIOmcFi47glbTF4/2fr78kGXc9NIK4GZrocobS9j+NuDsZmgALZOh9YTd/TBBjz3p6sQRQlBlBBEGUPrwNB/m76ApMtItl+xtNGJYGcYsez4dfljBK+C4f5llhIbG4cg1PZKUDIXLaS1qAhN11F9PjR/AD0QQA/40QNBIh11mJ75uHqRvG8x2knAQ7OjjfR+ud2WkTQR1fkr+sQwMXuwlFRXVgPxzHv9SyQJhE4LlGDFmCIKsFvaDcCnnz7IqaffhrPTpO9rqmXDmi+oNCsBOCvBxrnxfcEaT0iTdCbFeCgygiR4FIZlHynAV7JvLwvGHZqG7vGHMPQwhiEROTAXQ5ZRYq0QVFE+1F8Om4OOz19g/zPf0urx0DplFnmOhV0Cfp+Y9DgVOQ+QWeIj4YYbujhniloEQ+l9fSYQKKW28hsQIFk9lwEn/RNJcfDAAw8cLKNpGg8//DDrCjcxmRN6ra+ychO68TWhUCqyq5l2l5stoweREtVdO349IT8cvySBD1BVVYUJRCSZG554g+aY0aSFakgO1fHy1dMPko+6UJizDjQgm1XMHlHC8dUtTG6Ox5k8nrN/NvkxTMsyGg63UVy1mzVbHyLDuZ6faGCddipx3ljivLEMzv6Wn0M3DJ4sr8OuQFiRKDFreevrQmY1Kuyp3k9MYADR0XWdoc8QJbRwacytXDX6FSZWg5YocM1+J7bYGVRXNxAflcr0+rFMkCPcNeA9RgZOxFt3Pl/aG0jL3ErmyDmoAQOjYw+7/FOIDqWRXhumgyCqqLMpPwNxrw8XfsakVnHhxMkUfLKMcNpNZATh7Ip1nNa8ltJklXGTr/1/uldH8b8TR0nK74TuLClBfwhTEHE7ux9gdcI4DiMp0bkFdCz8nNbK/Uh//tvB7afnnX5Qj8JtwkTTwcS08UT0sOXEqkdQjQh6XCJmazPRSX0wdB1D1zAMnXAgB5vrt2UKlXQF2fbrZ5YhvyVNb3f2Plv+CWZIhV5y6fyESCSMJPVuvfBkZODJ6NmZtbJyE3uK5xJj69n5du2oRbwYvZYtl2zpNowcQNZFIsqvs6SYPSQzVJoNwsC+uhIQTExMrMn4T8OdCZ1Rqw8GPuWhjz7FqQpkhdxUOAL4bT9l2zZp3fMEU/tG0K6cj6zqpGVP4jNBYMiXm3HIR/ZtbUsHzlAj57Y281mncF7YMNCNMDUtqThWXXywbHKBBynRgxkOEt66nMp1y9Ff+xoQifH5EKRz2dl2NgPLipH6WNm1Z/Y9BeZ37ysgaqoVkv0zGIZBc/MyGpsWU131CaagY2/PYPCZT3RbjyzLDE8ZwLaawm73H46du55AUXSmT/+axeumYiAxYs1u7LpJniDz0PA+jE+NOeyI32ZJGZ37A1HOZhZ8X/XTVWKRHR1MFcOMYJoq6zaMROwkQWV6LC2uRI5tXkW/wAG+uvsLBLsTLSuWVZkNxLmbkPQWTARWetyscvlA+pJIyZPMzhl38NyNTUUASBW3UgFk/Kxr231V9IRqX4hLVhUTdElcrki8qeoIpomGxryvismTjsFwwOESOKqnFllUmFhtqQHH7h/Ls6fH4dDdPLg+m9EHjsEhFfBJ9jtctUBn+QQbXiA6nEhyv6UAKC4/F7nfPVjnqI1L+NFYgTe8hcEJIbb2H8GwiV9QWlPAno0n4k7XWdycQb5rFmZhBkVCHB+5p3NtZsxvuk9H8b8bR0nK74WfLCmHzR59bdbA7erGORXAJIJD7jqry8gcSEbmQFpWjGDa06fRODKbvjGHCEYYk1HRfZl10nNH1PfiD4shGi57cnrX7dcuxhuf9JsuR9ZtKPZfb0kJBzuQAHs3zq/dH6CC/ZdJiqpGEMVf347uEApZyyMOR0yPZdojPpyi0CNBAZA1CZRfbotggtmNZD7A6NR8lra2UBCZwAl3jMIWe+SsftbNeYhOldZz4li35Tv2R6r4NmofABcEh3JW8jCK7a/iccCWmiegBuLDMfQJ/wVn9EA0Q8ImH2k5+27FFiTBZHRpiGO8PrLPGsoQj5MlRoh5FdO5WN0GgKSYDN7j452KUk78Kp3WD37yt+i8X5LCdsccksQS3nyrHxOS4hl42d8Q3D3rV4i6DoeRFMNQ2V34N+rr52KaVp9Kkgc9EkTUeie6NknBFKCmqJzkvAzEbghZS0sFpllEKJSA0xGFpGmkhkxOiHay1h9kp8PkrKJSBldabWqMaDi0Ks74/h4MI0SKUE+KHEEWJCRRRhZk61u0viVBoL/TElLTdct5WMC0iKcpYr16FQQUwoktuOriOFYdyIemm3x7I395/SNK9xRSXlxE1Z5ClqsrOeBo5TjPNKZmn8hJA4/njfW38fr+5aC38MjKm5mc9C3pnRL+HcJINtaMwhuVQbTUhs3Rh7EDZlPeUIyJyVnDR3bpj7u3FfFhxTJ0ZTiazQ6yyZmyi4vz03hz136qIomkimnogkmzqSOUTeQjM45TU+eS4xNJrj2VfxFkUmQgO227WZnzHPAQ2aEqLo18Bd6v+KLiL3iFDsoTBb4a/Cwh1+kMbDqNU4tOJ33Q+zzsfIABjZ9SHD2Wu8q/YFC9m0L7PrApyFKIsGgjscZknejBHbKeiXzHD8AsBDOKfuYZvHTJkF8tIHgUfwwcJSm/E4I7dwJ0+QF1tFmaGB7PkTNIsEiKU+5+X2xiBs89vqXLNsM0CAjgPsxx1jRNXrpuSZdyFT9uJfO44QBoEUv3o0bXWF/SjEMRsQsmSeEgNpcd2WFHctoQFfmg2TkciSCZMkovEv0/Rzjow8WvJylmWMP8FZYUVY0gy79thvtzRCKWj8UvkRSX1Pv1KroMitZrGej0SemBpAz66wjMuQdY8W0pn9+/lhnXDSVmwM99IyQEWWfw6JMZPNpa83tI12htrSM+Pp3A/PfhFhs5bz2FlmJQvesJapRqNlc/ilElENSe5eNN8NeTItgPCzuvWb8GdyAXf60DiFC8dR7y1jdIClVwZ55J8dZUQpJC+BwX48RCPAKcnpLKu3S1HhWOHsDZ4qeYwCQ2sqtmEOq/X6G93zgYciPx/U88wjdL1HSwWUuB9QeWUVhyK5rQisORQVraLBITjsft7se6b89Gcve+ZDhk9DDWV27n1Y/fJEGK5pq/XY9y2HXu2TOX8orbkGWDwYPfR5IUJMFgZGw0d43N48FVB/ioqQXNJVNEEFEQsLmGY/rbKG3ZjmpoVJp+YoUODNM8pDpsHlIfNky4KF4i064wa2bP+Z4AZgJ7d27kwfebSOEAc+67HJfDzsDRxzBw9DEA7H77z0RFdvD8mc8RVjs489NRVKqH9aHh56qF1zPvzDnWfyUvr+24jMcuG8kFAw6lrMhNze9ybl03uGPBW/zQ+DxR6Ojxd3KyZywX90tmZFIUP26zIqp2mXEUBiIkelr4i7iIwSmpLKjJIN8fJivcwdVBmTKPyleGm9fHfkvzhlwyvv+ejWY+x/A6dzuWsylLRDGHsWtoPdCBZnOzrsCBM+Cg/4bzqJqcxfdF/2F97SC2xeSxIC2ap1Yuxsw0eHPMxVQLcVz9mUp6KEL2iA/xEOD6LR+R5vgPo3NHEX/ZWWRndc3afRR/fBwlKb8T9peVs/ysM/nqnnsQTRPJMNlvxoE4iCtWdzB68+vcLHyI265gijKmKENiiNUtb/PQRz8gizKmptNGLJozDUmUUETFmrmFBFIc8Yg2a3bs2rsYdenHBOxtGFFgj7FjaA6iKqtpihnON582M2zft/Q5ZzpGp+bKu84wu0vLD7b37MXzufHTQ6ZXEwFTlDAEGVOUmCjIBDeFWPbUXRiygC6JGLKI0fntRCcppgNGCZiCiMMfJn2iH+HTGwh87QRBojYjQpsnFgQ3AjKCICMK1ndH9H6C42Q+eOx4kjLG4vR6kGQ7smJDttmRFAeyYqetsQVR7Nkf5dcgHLb8ZXojKT4tiEfuPbrDZsgI9l/27RFMeiQpAINPySUuK4r5r+7g8+e3MO5kk0GnHw+AEQwhOhIR5Ooux0iSTHx8OgBmKACA6InFkzOR/JzT6RuoJ9i4lZY6i9g2hx0U3L+Qq3KaSI22U9EaJrqtAEWNwpOpY2oC/pokmr3DiW2sxLnVaq9DVxH7nsyPmQ0sXraWUJLI+XeLYJp4gpYhqcW7m/jo8WhRe9Fak9i36AH22HYxU3sMR+EsWoQ4fDEFGN4MzOgMRHc8WapOadsadswdj+qsRwmkkDvgDnLyuvoXCKKEcJhH9b6qNm7aWoKJ9TKzYflfMfQU6iJ1YKgsmbuEBK+bGFHASzHDhX8hqnaSk64jNcVKpSAKBgISF8zZws6dDdjsEpKqM3RIEt9eMAYYBljpA05b+RU2weDziWf3ep9XrT4WVW3HNEwM08A0DExT6/y/DqaBYeiAQXSchz9PuoS40DQCGz+n3deKEfZjqkFQA7QEdtPuDtMWbKKxbetBgjK86nhUMUxN1H5q9H28+tVVjBvzEEZnH/VE8xv8bTy54mMWVX2JJlcRLRfQru3hr/2SuabgUCh3VEIImjm40rXB0Rlp2AxzO392lVIC5WIaAgaR3GhK7XFc9+FOTHYy88x/ATbuy10KwI6ScmiGItXBvoz3eN6XTN/N1u/vvSX30F/eSVTMf3jQ+AdagsTMM/9FTnorhYMHMaq0iAMt/Rlbv4e8YZWURfkpPKGY89V+xN18HYL7KEH5v4ijJOV3gnjpJWjLljHWG4Wma+iaTrQG8wMwMMbgGLWYSfomdghjMA0dQe84eOyOUB0aJpoWJiBU4YuUYPzsn+kzMTq1EfqEOpCWXsPGyfEQFMg50arH0Gw0ffEiANu2uNm25ZBw3AmxXu5IjiesGTxWvItAaibyzf/ECKsYkQhEVMxIBFQVIiqNHVWo0S1gqKBqmKqKoOmgaYiqTsK+amzV4B/nQDQMJIdJOFbFI6gQCSFgUJ4joMsd2OrtmKLZ+TFANDFGGxh2lRSlhJIfIrSXezD1bjI1p2YjRf220NCfI9IZreJy9SwA1aGG8PTinAtg1xVEW+QXzyeYJkIvIcYAKYPiGHYGrPmkjgUffEjtjh3kN/kxzZGIdi9SVM9tMUNWxJNwmP+P5ErCk3UinqwTKR0Dlz/yCos7MnmtxLpmr+HlWtXBkDMzOfZka5B67apvcQ0agH3YTFoFP+6FT2P4atF/+Bguh7jAYa8PQcARsdEYEyHPrqNFWeHCkUgMGDI1oWF8HDWbTNs2YpsjpPpq8LTuxFPecdAldb1eQMmekzjdtBPlzyZ14vhD12SaRCLthM0QZshADURQXDa2V7ey2QUzgiIiEAEimISdNgx7Iq2EaZBltpsSARR0cQQXaGfzyJTbcbmiD9YtiQZfbq1lZ1UDAEZYRxAF9la0HdG/VnDWLy8p1GyeTt3OY9n10ZJfLGvhRa5IuhTHzg+O2LOhMyrur9+fxZunL+SFsVdxw9rXGFf+s1QPcXv5cusrtCtOpklu9u/LhMMsKb5wiFsWPM/a5k8whQhx8nCuHnYnuek5XDPvtCMGhFasZ+nby5MY5smEJw/t22jkM1osZlL4uYN+xdOd5SybP4Zsd5B0fxOT2lawc/hC3nlSw6ZDSZ8EcsY34g2LvBm5lkLHEF6P/zsVn41g77i/s9l8j/8Y5wIwNRxhXHUFa7wahQwioaWepdlx+PLTKDFT8I/KYLE2hkdnjEeQjw5l/1dx9M7+ThCdTiRF4aTbbu2y/adk7OVf74QtMPCW75A6sxLv+FkddU+MJijHkH3boi7bH33gYcb1HcnUWSehh4OIgQC+ykpoO48+7otJiB2Gf28xxcKb5B3/N8yWPB5x/5XxziCjYnKQRIHLx2fgtFmPwzOFW/FEuel38Vn/z9dbe9UpBHaXMPz6bT0XWphLP2UKWbPf7HZ3efFS9lZeQc6JVTjUkxg1+RHUSAA1HECLBNHUIO+/8TmC+JtUu45AVct2fiw9hUX1nyEJAqIoIAoCkiCgSKDIAitL+yMIGvcu+AaXTcGl2PDYbXhsdqIcDtw2hVzTjjMYQPcFEVz2LonaDocA0I2fRJeueX8O+1YnIbv2E+mArdt/pN7Zl7GJCtEDg8See3KPx4YaLB+IeRufRqrsQ07KCAb17zrr3+e3lkyixSAvn57G0x+3o2MybsqhXDIGIrLDxruyFR40/MTjKPjiA5ybBILDRPIifcgvP8D+dD8nr00msc3OxoIWrpxqieMFGnIoWXwWP/GxhKpzKFDOo2LUEwQLb0UNJvF9rsm9JX5CaJh7rOdvcMp27GMeonAdgIkgRJDlCKJogBsa6vvw1BNPMjxzIK3uONIcKs/MnEqsvXey2uCr4JgNlcQmDD1IUIBOawYYpojglvn+pslc+tEmmpqC3HbSkfpFumki/QqSogX64IhpoP9kBwICgggCorXkK4qIgoAgiDRsa+HA3kS8cj2t0x/FHpeJEhWP5PQiOb3IziieXnAcf2tuYVNbC2PfH0XIFPjipLcIjtRZ8dKh3N5acz9IqSFKDRKlBFGWbuaRta1EpbhobW+gSFjMtrTv6ec8mXsnX8fIdOt+f11mKWE/8l0Fn2xcz39OHog30s4X63dCzEBWzXmNLZ4KUvsdzwnFqwgJBh9q0/lGGN/lmvO3bmRDziAu/tf5XLvubd4PvUy4Eg7oVlLIllA/vigchBs7x5WvYFhqIxulCaRNEykN6ryln3uwrgGhKPJrN9HokQnErCS9yI89rGAP+4mOasNcJvDB9aPxHCUo/6dx9O7+TvglvQRDDWAgHCQo3UHWAhiuI6NUNFNHsSmINgnR5gGvB/zN0AbeuMnE9DuemHxI5y4AInqIPy0v4qIUH7MGHJngLCxJOP5nvqjogTDiLyx9GAKIvRCMrPypeGO/Z/36U9BFDYcrFoerq0lXts//f40OPYh11anMLRlPoqsVwxS6fHRDQjVkVOMMQOS9GrCSGoQ6PxYyEfkIDxk7Y6nZuRHTNECPgKFimhqYOoKgg2BgU1Io3dvKmiteQUlORJQ6iZEEoiTijDMp355A5ogKkjLcrHoXjj//apZ99g4Lq9/lhGEzie9FTrWwrZwE4KGOzYTDW6DsK6avf5qbJ/yDnOzjQLbxza0nEQwGSMnIRgD2ffguSqzE/PUfktSvH+PTxmMIErJNYtyYcaxdt5bMc2bgHiXj//s7xHwk4erXwW0Zl5M/+lRq2krIPDuPU0yJHWsfIS7pa66P/xvR09bRmDgEu2bnT2URhpeoZG66k0qnwKzj3YQFcBzwc/irqLg5j1HKuIPhI5omIUkxuF2JOJzxZET3xd1azsaKHRiCyWmAfuJE6IWkhELVnLp+A0EhlzxvYpd9Ed3yI5IkEdOv8dbWStZeO7nHugx+3SMnCDIOj8rEU3oXL/x24RvEOuC8R89A6SYnFECmDA8nqSymgIX1JQAUz76E3DqYLDtZMelfB8tOUPNZrRQDoIsRCOpECtuIM+wMUyZy1QWXMDVnaJf6Qx2WxcShyxQXNnJJ+2aacm2EYgaSUl9JQ00Ukjya48NT2SRdwiLPSobGFpGXKHLh07fybd4kBpcfYMeEkQxrqkEt38uUKWfA92+xLn4U199V39kpVUz2h/Gum0ZSSxVmisR2dTDXTT2bgVUVXLcxiC6KZIRfwSfczo4h07jMk8ure94lSZuJylcICJyMl/DgFIZlpnEU/7dxlKT8TjBNs8eZNQBqEBWF3hYUFCOILnfV8tAiKoZgdsm7A6B2OoMq9iPDav0R64Xk6sHHIizJOP6HHvJGIIzk6PnxMrQQpiggSt2/lH9CbGKeNQM1um/ruqQ+lMUn88O8ZdgARQCbIGAXBWyiiF0UsIsidknELoo4JRGHJOGQJJyy9SkNjMclamy476Ie22GaJpphElDD+EIh2sJB2kNB2sMhfOEwlTWtBJ98ndDwsaT2z8EIqZhhDSOsY0Z0TM3A1ADNpE41qddMAjhQ2g1MQ8BKJChiml6ogPicKk658mLWfmcNPgNPO460vAK+fvI+vvn6PSaV7WXUPfcedMRurSll45wX8e3dg3dHKUEbrLp0PcX75vLfTc+w2Gxn8aq/Ebfc4NNzfyApIZ2YzmvzFW2mI5JJqO4ZeAmeHV/D4ps3YQgykk3h5Bknc/IMy3KjDsxn91efY9vgY/D2vdj1MkJN+4gF6pY1sriPwoPJlyIY55FQdTUA3uYS2hNuIDtgYgoaT/R381mW9bwmRpq4LHM+RVVJLDKsHDSbIl5uGPMk7uieo3gKRo1lUlUT/1q7BmXHRgJG76HfdnsqA9hNGbnEBRtof30kUnstmt2OarOTJ/gZEgPbVRdfri3nsSn5PdYV0kKEtQa+/+FyBEE69EHq8v/G/Sf12iaA8JavKW/LIV5uRqivgD79usT2Gs3NGB0+tFCQVLudZ2Z+Q0ugknUln9HnsVdpO7YP0rSZDKnYQOPKCEmNW8k+9mYG6BnsdLQx4e6pRMdZzurXPnA/aW39DhKUooZq3tu8BEkUKe8oBeCxE/O568smmso6EGK8/GPBy8itNbQfNxtPQgwV5YVo+Z9wTIxVPgzUPGhy0jMrUVpE1JEnM/O8KQiShCRJNMfOY8LHp0FsOjlNwzip+HIinm04Ynfhr/lJNsFJQU0pg6v34wkNBSS8e+2kBR5nS7YX3LczqdDF/r65SLZbOTFKpA6BCReO+cX+PYo/Po6SlN8Jpmn2GhpnqkE05F5Jis0Iov9M8j7SZs3mxXBXS40a7iQpjm5IitZJUnqIVgnLMo5eFE9/DYxgBNnd8+NlqpbPzS+RFABB0pGE7i0HRSl98DmcKBi0ABoCKqCZApouoBkiuiCg6SK6IHYOACagdX5AljzY7IHe2yAIKJJAtOQk2uEkna4Wnf1mMZHK9YSvvYCEXpZiADKAhuuuwN9ex9VvdM1k/N4jjxBs7ssZN5yBJEnoquXjIslOEocVcOl/3+TbO25i2fb11F59GSc/+wpbazbhu+hqUtugKV4hHOOi47rzsNtdDBl0Hs8POo9tuz5hWdEcXvMV0xGoJykq/VDbl21F5JCFzhBA13QQRKTDIrhK529A9/tJueY1mjdYuW6l5EOCcLbdTaxwOCFRJqrxUAi8PbgBOXKAFlsG0ql+kna6iFY7uKfkv1xc8x0SBnuixtHQegzlGIxG5r0nFtAwJIG/nj6OKHf39z42PR7Fa5F2o0cXUQuCIPDY2Kso2bSRhB+eweUrQbfL6FHJOIN+EpqCBFxttAfDJCT2npjRj4csytC1XSAYgNGZK8c8+LeACRwiKeFAhC+eeIU+Q/phd7moKNxH5oB8Bg3qS3/H1/S1T6b2lTokcReyM4Boi2BqIqGObEBkzYlxrGUijvWfoRoQvyfIZUD08jL2+jdhmiYDijcAsHvUs2Cz4bA5KN3TjiQ7kWQHBYpCwObj5iU3c9mgy/jT3OsxxEO+b6Yh8+I3RRjhaG6VviRtpYNB69YD8OmQAzwz5FxIzeWV8Avsqx/CkITdyKJOh03AdVmQ2t3wjnAbT73noaAsirwqF1GtIQzzGJ6f24Bbr8TgPjbmNbOyv42CllRcB3bgNT1MPrCT3Z4BrPUEGRFxMK65g4pEDwumnIW55lNOq/iRkCOWQGY2peF6XCedh/g/fEcdxR8DR0nK7wTD+P+x99ZhUlxp2PevtG3cHQZ3dwuSBKIkIe7uuxvZ2MZdN7bxbNyFJBAhARIIBHf3mWFgZhiXnrbS749qGIYRyO775nt3l/u6+qrurlOnTvldj9yP1S5JEfQwZnu1QWwbxY5gH0JSIlEz7fdb5tOpsCfJ+Y5mhRG1pMiuVgrQ6fstKa3rs2iSgrudUu1HAjOk40pu2yVhalGS0kaK9X5YloUo20h262mnomAzOlzP+6e1TwzAIYoR0ySo6QR0naBuENB1btzpp0r99y6FQFUNCuBOPrIMA0PTWq3/k9LjG2QpHY/vXKedoSFI1gErnOrzccbL/2TxM0+wbMVvVF96LiXeAk6vg5qn/sK4U1tX2+zf+xyC/jLe3LadWcv+Tn5MDorsRpHdFG9PISelnOGPv8G8jbN4u98E9PpGALatrSP4+Df0HJtH6Ganmm5NtE9p2JXI00aROSGHTz7dxK2ZTT5Cl+dM7PDGA79jat6lOO1eMkZO5r7RCic9dzyD65sCtzuFipnxhHMMF9w/n3AkhSfWBVDV1dwxrWUxx/1INaASmPzyIubdOJasmLbPOTWic0/VEob4N7Ok/3WMPP0JXEA45Icnc6jy+0CG+6f0aLMPgJCWjKqM48QT72y33d6lswhVhKndV8OXjz5NQ8UGSrfur00ksWu5yXwk+icfh+Cy8Q01EMrDGHUGZsQFFsT3qUDJSeBH62RWMYyMYB2KYJHlc4Jha9KcqpeiKLK9Tyey43ZgeqqQRAPLVUZV6NcD4+k3Eiprsvm0uJafi38GEVKNE/nqgrsIGyaq6CLpqWxwg23D1p8dV4orQefYcBzbl81hdd/+3LTwcWff+0owTReBxPko8Ss5LaeRk8trWJjRl7R9lZiCieBOwvZMYaXvB+o8W1jYV6c2VuTs9eMw8uKJ3VeNFI6QqFfTIVTM6oSBrPEt57WTh/DdMVOxpXi6iR34LD8FT/F2RjKHea77OHd4y1pIR/HfiaMk5Q/CgsVL0EMhbn/iKWxRBEEEUXS+iyJP1H6FjMXGJyZiiTKWqGILMraoYEsKiBKDsdhcuJcvvvwGKWpOlQ5yIf3jvVcPSIQ3RhzFTcXTUpdkXyTifGmjSJ0lgPhvkhQrbCC2UeMGWtZ/aQtaOEq25NbbaaKE2z68Ngk4b9NuWcYtyyTRRI5EdqIo/972huv9KIAvqW3BsoOh6xpSa/tYMBAPOi6mriEcYiQQBIHRt91F+vffMuud14ivz6HOs4/yb7+CNkgKQF7GQDxbbF6vWwd1TkCzV4vjosCDpA0qIy0xi3PGOtWzGwpLkYwQdZ486naD/cMz7Jf702OSSX/hJdJHDzjQ90y3iarbHI/KE8M6kZIwgDsWLOeHwh8AEM065LDA3pI95OV2ot+5L1A26zkS/CHQTbj4Car3lfDb1ysYEUnmVSEMNry6opbPVn3Bbcd1pV/3bOpNnepwhMpQmKpwhI2hCJH0PCK7NcY+v4CXLhhI3+QYbp27laLyRs4dkkufNAVhwcOMK/iCCbbN8i5n0e2YpqKdRjQm5dzju/PcqOORD5N5ZVoWtnl4bR6tbh2Rqt94+y9vAZCcOwxvQgp9J46l2/De7Fy+hfVzFtLJ35lKbxUdpk1rs6/Fc5MRAgZXd+5LIGzgSujL2jf6kxuqIcay2B2qonPgI0RviOHZpyGLKrY3EzHtGEw9jK4H+WXjlZjx9Zybcy4+xcc/vlMoCOXiEj0kxLgwdJ2d/iTckkGWx7nuJNUi//hKOomP0q2xEw/9VsNMRgMClYFsFKUBvexMJjUIPGJOByBp4y52NqYgD8rBKpyGCIiu85kx7C4mVWeQua0voTjnOinPTuPy8ARWV82hQJToi8QpWj3P9d1MSskcOlROoCTjWNKBUF435jScRGogRLekIyuvcRT/+ThKUv4gSD370LhuNbFdu2OaFrZlYlmWI09vWWwOdKWfto2A6EWxTUQjjGTpSLaOZBtIlkGJnU6ZkE7jrh0IloVgWYi2xf5HWlcjk713LmRbnIR7+DwQYM6vE0nIvJmRfc47MJZtIecG+1lZJSdGa5cV1heztmY3w1M6E3J72BhqaHNb7EiYkgsmYfpDCJLofGQJQXamSBJGo4bYTnBnJOTUlyku/4zgoq2IohtRVBFFFUFUEUUFQVTRRMddFQzupWjLLGTFg6R6kWUvquolIst4zMOn/baHiG4iHybb5nAI1zcQC/gSj4ykmIaB3FpWgmA2030xDZ22Cjx3OekULurWnS//djOLu+Xgz86gPeWO7I7HsPyyjZiWiW5E0PUA73/5PZZgM+LkqQfaffnESsqLGiBq5VIFjdRKR18l9cPppAzp1azfr9eU8FuczWmGi5enNM1Lch8kQqd0YWSZQcPrW5k7cC3uWB966FjSK+OIM3zw7G4ABpHA+0I9Q0/uyEffOv/VWF7u+qkEfiohMiIVO/4gcp2UAUkZPDc2lftmbOLa15c7Qa0CyD6FF6Zv4gXlJaZKi1ne/wY6TbyJYfHN1ZUNQ0OzuuKSlMMSFAALgX1lbcvK74fsrUFQvfSfdB7JWekMOL65Raj7yN4opbWoK2zyzmgZwH4wXJvqEMpCPLykstn/f1v+PicIyxiYFSE+L4ioABvuPzD/15FJ5Kt/IW/CTZhbTSxT4u4RdwPw/BffA+BRXcyavZCV0z/EW+cUm8z0NHDamZtYIaTRbX92llzAi/IrjDM38lrMpeysdfPMif35y4xCtgZHYrmmIwJ9EsrZ2ZjCtNpz2eEKsTki81Xf55ggDOeaNxaxelAaO7rFY2FheP08lvxPeiXk06OknPyq9xi4Yh35SSJJfpvLh36P7fuemvrjKdBP4bljsripsuCoquz/EI6SlD8ISt9BzEruwNrRvVudf8/ykRwfaGDtsB5ktOGD7/nYHJKzYvjt0pEt5n335SYGrHQM8d0bTCKLr6Jo9N9QpWqCFffw8y/3UNKYQ4a3lA6iCMJn/BRIpdO8RQTZ74OPhSInCj9dbPvBb+zdjn9jDd5cN6JPxTZMLM3ADprYpo1tWrhSVLxjj22zD110iFKtVElDYA6mRPNiIIdAc/3IrrIfW/wfcH1BoV3W5nJHgspix/V0uLih9mBWOPstJjEOU9OpKawC20Z1SyhuCUURkZMSD/RvGDqyuxVLk9icpGxbvAcjLHL/009iidG4Gl1DDPgRAn7EQCMe1YViaCi76pgw/ytmjj6R2HaK9UmihKR6cateGrariOmVJMY3xS7J+7OyBEhM93L6bWMQI0sw/QG8HbJb9DentA5ZgacndWOvP8zPRdXotk2scjp5sZspatyJHTuQCVd2Z927c+myIh0ZiXJXmOJONcRkOhWDRVXi6a3FbK6W2Tr6ZEb26sCMJVtYVVxPSrKPj1bVMs0tc0F+MukxPjJiY3hiYwlvNNQzvkMyi28+hqcXFmDJAmf3yWRQaizf76xk8EfbCaPSL3Mq2oxFBNwRXKPGIEfrOZkLV1GhPYfsP7KUNlsQyEg9fBkJ2zIQPS4mXdp6Kr/eGEJfXk8Ind59284mAhjTYFPvc3P7RQOQJYGIYXHZ68vg2Ankak3XhW65sAFVdKylpiSwp+w9Gj5fRUrSPvbh6OL4w/qBZWZ8+y07P3wdLxDM7kO3DpVUrNR5e98gOtCAnV3K5mG/4EtNJPX7EZwpLaDO7+MRLuKDX1YBSYRxU507lNQ9KyhPM9iW3of1e9fh0jU6u+HkJWVMWucIEA7eXEif/PN5Ov0NaiIFhN0JLIpbC5mQ4FfIKu/Es5/UYQerMb9VqHhIZ2DdKF7p7bh4xvU4GjD7v4SjJOUPgmHb7YmMEjad7IQYpe1DYlo2rjbe9E4+szc/DqjgT/9cwZ86pjJ6TxbhimeoT11PuuAox2bH7I22thhvz2WZMJpLk6oxrGoaLUdO/81q5+23S/164JRW12XVO7VuUm68Ed/UK9rZ6raRkn0Kk7Kb+rdNE9sMYRshLDOMbYYpX/co282FDOj+BcFgPYYewjDCmEYIMzr1RkJkuiP/0hgORXFjhA6x/5rmSuyP31CaOYp1N35CnZKFeUjNJWyLeLOKDtk2cTlJVIkyMbqbfcu3gA22ZWFYAcpJYn2Zm/lffoAiCWyL606/hrVECnfiFcFl29iSjOmLRUhKxd2xC67YOJKTklgcA1usLPouWMIj6Qb5Pi8+1UeMGoNPchHricOt+pCjAdNFJSXEVWeQODncbKj5/ZMp2VaLL3Yb+f26EfE3kpCRBEkJrW57wLKI1WymfbqaLZsqD5l7NrYqEslJYVZ9Ieffcg6RUJiwFiQnvrnc/7w3H8QqlHF7HPKQnhjL1Sc6svDLyur4aNUieqWlMLJj7oFl9Oh145ZFEj0qT03u2azP0eufIkWook6/jMZvNCDFmbG6ENX9GzFDfIQXO8fqh/V7+bjoLSe/WHAyrpzq0041ale1hq9cZEJKN9J1nffW/YQgCjjeWyeNXBAFJ6VcEvEXbG/jbAHLtCh5bCk+IQ5OPnypCNuGNFlkXEeHZGjRgqXJPtNRsItCESNELMcVUkoaWzaPpFuPJYTlxUhAws5qli/+kr6DpjDrlgGc8PwyZq7bzPXpu4iTI1RTxoi/rGLlwnksePNZXJ5aCiPDmf9tPVDPNenO/t5rpzI2rpyFDQ5xeOecLpgrnPpRG9MmMLPjufw4yGZMhcYTx6bRYdybTduuNbB78W3cHX23KE6tpjBd4OWTRcZty2Vc779Cb6iddy9CoIJeW/wU9buHi+yerF8+icHX3njY/XUU/z04SlL+IHxSVsM+TW9zfsSywLbxtuN2sEwbdzuxEyagA6njcxncY7/K5OmAY/41TIu7Pn2fxYU2i+65rNU+HtI0tvbrj3zLhDbXY/sdi40Yl9Bmm98LQZIQpBhQY9i/hYLsA8MmKXMAyW2kb8fP/xl3K8Xyfg/OOrYTX8wtIMX9ryvXhq68ja0LRLx6NT2z/WR2V5EkAV2z0QwI14fYukFkc6kPo1xhZNqpZHk7Y3xVdaCPjWlLeKCiKRjTp4TQ3YP5NX8kYrzI1tuOQ21HuOoi4NjiDdywS+HOvbtILL+l1XaK4cjXT9l2E2nkU/ejzDvffYmEhSiYRAQFpCRqSzax+LPvWfVdDhld+pHVrSvuGB8Djh/RLJ0+YFvUmwaNUYLSq08qdx7TBUGAx+ftZPOmSuLsam4pTOPvhV/zZr/+DErp1GxMmhEh4++f4hl2CYmJLWOMrKjO0KEcfUVRLaTK9Pl1A4INp8TE8OrYJgE20e0Emoctx/qYcWkcQlI64YVL8K8SqfktAefKAa+pU2tb2BY4zNGOysE7313BRoIxAXLrOxKr2zSaJtjCgab7v++fCkjYmOz88TcQRYf3iA7pCa+vId5yrFehshr2vP0YmiVRnTyRzUERSXGh6SaaYRExLIqCEbKVpvPTaHSy0cp+Wsl7Hd9gUMxXVOsdqDI68ovb5Hrf++wlk8qaTlQvzcXrqyeGOkbNXIlSfi8r8p5m3OxlJCVUM7RhC4OTHCtHV6qxTJ0hYycwaNQ49rx5NUl7fwGgh+dnJHSWxUzi7r/8nV/ef+wASVm8aRcnDb0OSh5jSvkyXk23eHFlkPygjb3RKbeRcPFfMRJ1Kt57l65ljpKvIUvkVZrkVdqUGGO4oHNTlWzJm4PYqYKMYATPKolFsZ0ZnJWBK/7wGYFH8d+DoyTlD0J7VhSAiGWDRbtaKpZl427HZ+7XnJttbCuZKh8t+I4F28vYXKGitDMYK0qkRFfbNwKrMRr0Gvt/t1aGZQSQLFoUozsYGhKu9vRnjgD1YQNbEvD9G8GzodxuwE6G3jKePr1TW20zLDqt3bKb0regIUYn6fhM561dAHHreKio5IOzUhg9cMABt8/xT3zFVjOGq39ZwuuTRuFqJ6j5pOze3LBrPceaLi4a8Dw1TzwCNeVEFJCmnojpVQkJITQhgpldhFiYQbeYekzTJhyqIuxvgICLRl8hllGMOzaXsH8Pu9ftZXdUPPi3TzLI6jGQydecj+rxELRtUpCojY7hhJ7pjMt1rCSPRf9bPmYij29dzEdVqZyydg8zBsoMSc47MO66kiIAqj3xWK0EdBtRGRT5kCji8zqmcv/aIoZ2SmaJFmZNNNttP5JOfpCaoVey78U5WLKfnB6OW8U37XS8p2pYNVXU1VuE3i5k6ujh5I9tHm9zMGZ98SvLNs3jL38dTnxy+2nKALOvXceG2gWs+OxLhiRPxm/Usqj8GyKWQzD6Jo6lc+xAum4668Ayb+3sz8dR0iQBCk4togZskJqCdW2HSSFoEQYsfIaIEsP3/Sfza3IGFZJNin0S+zN0LUuh0Z9CIyko5asQJAv/QD+rfryZ+/Pq6b9lOfOVvvTudRyp656l9NkTybltDqIkIWm1xMo1XJ92OnpAYk3q6Qy/8V3WzV1McGEts5b+la2Jebhtk7QdTgr0N+LpvL8kgGiEIC0esc7Z3khNBQ2ffILt8aBfdy0rv17IyH2bDmzT2uSe7E6PcLEu4A7ZrO9+OQUM4ib7ZZ4xzqOLK4OTTmoiMUfxv4GjJOUPwrTqBbzpGcANP36GIjiiY7IAiiAgCzDX6oAoyix5/x4ESQFRxZ2QzoAplx2I1TBtm12VjTyzpIDShjC1YR1ZkTgmPwmvLLG0tA6AlFZIyjtLKikPxJPijTC2S9sWAzuqy9E+SXHegsTYhH9xbxwZTCuIZLXP7nT73ycp/oiBIP97gXiBRme/xbVTU2c/EnrkUSXuRshIJGNYk3tii78QNlaSndKtWVxKnCwTEw7xo5JG13lr6GNrHJscx5ldO5IX2zwlfb+oWf+ENCb1H8aG+meRdzsPt4b3l9F3xgy8SYdWVYbvX3oNc8kKOmzfSpw/wI+DB2Jjc8ULf2fd3KX89vGzztizBlBXupbda2fxxnWzANhz3t2gKMgxCilaJVfJK4AOhHUTlyJiC5DgUnmy/3iu8lczZkURj6xZyJs9hqG43CguL7UVTpCsjcAAb0tLihHdrkMP9RXDOnDFsA4AdP1hFYYWYfeeMjrkNtWrSUrPZZ23K1LUK9iwt5yYzBRERUVKz8JsKI323f555PKo0TG2Lxy3H7kdxpMbP4hYwTmWHjmG04feinpcKoUbdjBr03yyT06gquEBUpY8QEnGiXwQVjGzvWw8eSBJnqZzoO/9PyF5DrquVWcsoiSh6o3k3HI9sSUyFY02imkwTpGp9/upFARqJIkuikTi7C8RZIvk02rpIUdg6dsMBmoUmS7nvkxqp+Ow1j5LfP16Sr77B7IvHqW+kKqGJIJrIFDmxsMyZn8+hdyK3exP1O5RWwwzYPGQQTyZexqdwz1JBHx/HkRiVgqBNWtp+BxC372PKEl0n/ENsZmZzM3ozxULi6hXYwiozv3GFF3MHhVDfMDk0p8bkHxp3G/dyIiOMVx++eWtZ8QdxX81jpKUPwgTSuewNlWhxJOMYQvoiBiIzlQQiaWREbU76FnwDrJtEiM4b4QNA8YTl+mYxm2PRF2xn5eKtzTre86C3c1+Z/maPygXbl7Ozto0Tu1ZzouXXN7uOK2Is15RPbwlRYhruyDf/wmYZujwJAUJV1vpL0eIxoiB9G9m9wQbHQtUQvzhSYpl2CgCmJ7ml5+mO2/QrkMsOhHDJkOyeC4zhm+Ky1iuWTxVp/PUyp3khxuZmuDh7O6d6ZSUgD/ikKXY6EOs17czKfr+e2oef4K4yip2jxqNPzMTy+UClwu8XsSsLNxr1hGja1Tld6M8Ph5x1BCC837E7fPS/7hR7FyxkjHnTKND386smb2IQJ2fLQt/pqFiC5XxHqTdjcgBnaWu6+FrIH80j67xs3ZtOQKO3s3Solqe3bgbYiU6Lspj2313klyzufk+kBTcasvjGQ09OWzNHLV8F1/+9WE8x5zFJZedi8/jYtXqrXSNapYV3f4zsqhSqC7A2z0FY6Mft+lFEV0EA3WHPXYAttB2+vHeOxcCkHhWNyxJQJTczN7zDsdnX+osW6GR2rcT+8qqsRUXOZ26EJ8+DCbfTDaQ8s0SyuLd9Fq6haxGixcH5jMmJxHTtjl4rfescK55wXDOu4onn+IsIK97P+78051cqaczceViavM78fWFpxHUgzxb/CU9ijRS5QiV3kHEpA8gMGwqcV2GkaQ4cSx7Y0aQF1hK7Mp7AChdlkBNYVO6786Rx2EZNjuyO9DjmsvpOGIAO594GOHT6Vgbg6zL6UJ8dKQ+n2NtkhObLIt7cnKwt5by6y9f8I7xJgwF9DgukLuSG7+XB+KfBqDeJ7Fj2FaGLHMzYUpPjj/++MOSyKP478RRkvIHYWS3IYxc8yzcsrmdVpMAR+di5dzPGfLbVRwskvHD9aMJRQy8UTn3HK/K8tJ6ArpJ0LAIGiapXoW8xOYaAh8v3QikM7prJoeDGXHu5lI7lhQ7EBVii/+/S1KqghoBr8APs0bgnKoyjgFcRhBUBFwEXX/jpdpOhGdvdqTwBRGX5Ejhq6KAW3Ik8VVJxCWLuGQJlyziliVciohbEakPakiSSENjBK9b+ZfSkUMBHQub2JjWtWcOhhbQkQUBvM0tWpGoT8PlOoS8mDaqKDC5Rxcm9+gCwM7yCj7fUcSssM3zYYnn1xaSH/ITr8igeImJxulIkkSnU07BNWIsFa++iF5SghUMQTgMoRAJa9bAmjXsD90MXHkNp15yPs++9gp69Bxwe91c8MjtB8Yz8PjRAIw5ewo7V22hdGcl07f6SZQ1uPR7ePckeLYH/UYtPrBMzhfLsNI8eFU4bXEpXcvcGGcMoSpjBJauYWoRZI8PbZULl+Ls//Xf/I20De+D7aWfpbNAlfiy7kOgKcNI1yPs2uWIwmUbFaiGU2wv+OsXvPzrdALuRDymwZSUk3HHxGGkmCTsU0nWMmAD+HWbcmkvBfvWkGMcT/Ow2+awLefheyRKp2aDdiCT66K3XmHTK9+Tsi+VhKmdASdwFkA8KFC+IRAmRgtAVMOnNEbk9nVFXFgbIKiZBKPlLGzb5su1pUhA5/rSZust79qFHZ9NZu9C59p84Mq/cMb7X+EWwH3MQ7zcv5hHax4iNbgaCldjVZeh3jL+wPK5t3yPUVeC1liP6/Vx1EcJii9XJvuDb+mZ0bHZ+moq97Jx2df0FKEq3jmLJgxKhtUhqnftI3NQPlJSCrGnvcHiyG9sjQny86JPWJC5AJfgIs1vsiehgR3FKzn3KZN551/A7vgOLE0YyHFVi1BzJzBk8qVHU47/h3GUpPxREMRodN2RwTYc27SoNr2Z94lv6Qcfk9fSdH8oQjr0Ti3jnFFXHratEXaURvcHHLYGKxgAwUbwtO2X3/jR1+zeHkIUQBQtRNHG0a2zkUQbUbCJTXbT//Jz2+yjrqIj5YpKekYytq1j2xq2rYOtYxPCthvYX0fgJzOCZoEm4ExF0EXBiSQ+DOSQhtyg0e8Rp7q0aDvxADKC45ZDiLrlBBTR+aii6EwlEUUS6FoaIRWZl978DEWVUFwyiiqjumTcbhW3S8XtduF1u1GqIRUwA0HCkRCq4lRM1qMZG+ohloRgWCNGbU6cuqSn8bf0NP4G7KyuZfquYn6KCFRGNDKCIdLjkuh45/eHbOkQVj19K8kH1cTZ8sEH8OhjB37nP/4wXHI+RiiIoR7eKtRlcE9uyk5n+polnNjRDQlNcSZz5s8kiQEoWT46ur1Mi0/hxN5p1Asa3xQ34rZ7MuLC05r1F1nzKR5F4Icty3my5hvM7DjuC3TDTTzjqr/hjEWXU7JYwG0E8ZghvGbogNthPnBRzv1c/OrHFBYWs2HVGqy9e9DCYZRLh9Kjl0PwLMNk+zs/oxc10vXO41i7cgllb35JR1f7BHP/1bv08a9obJAQgDNevbBZm+xHRmMFdcRYFVaVI9igqC4G3NRcwWY/SakLR7j5lR/ZXB6iNKISntw8xbvAK/BYSQUyEOuS6Hr/xxi6i/Hd4li4D/4x8gpuWv8emseL59ZbWbv4RvZ+1/Ty4MrKRBAEgrZAjSJSkNWb0/L+wbfrohkyFz7TbH2CJCMnd0BOhp2VeYBOyrtvkTqipepvXX0FSy+ZSudSi/cmiSSNn8jpls3YERlYq3dRs7KYzEH5B3bcpGOmMSzb5qtd0wlFQnwy8kP6dOtP3/f6EsjwUnVpT8YFvqFndQlTqh2Ca/9t1lGC8j+OoyTlj4IgEk0bODKYDkm5+dWfuOH0cQzo1YnynbUE6sPYklPqHYnoVCA1w0dcXMv0WcuyWFiUzICM8iNarRWOWlLUthUdrUAAUabdm8fG5X40002ipxbTFrFtEcsWsWwJ05YIayqBXSn0OieA4mud7GiNMURCPZhyydOtzg81BFn52CoCY3x0P3lQi/mODL5FWDPRdJOwZhLWTSK6STiaNRExTLZqQepEi+RBmQfmR3RnXsSwiOgWmmkRMS100/mumU7fjbqJbtn0tCKATHiLC82UkUwZ2VIQDtSU0aOfRlJkgdQYGdfGeqo2rsTAJCJqzLcq+E59mopn72KfqGAKCqaoMDUmzE8xsZz1wWfIgowsys5UkLENm/RQOh3sDpwnCASDQWpra0nvfl6L/QHw7FcLefSyEw787n7BBWx88ikUw2Bn/0F0Wbea7154BTMUwnQfWRZFdloCSVYjH+3wEXppBs9G/6+23UzrlsHdlw9s1j5m8ghiP/6Y2prmJCisGTRmreSDmC18tGwPQjT+4MvYvbx43icsnu7DtgwMxYupxCC5YpDdMbjc8eQsfI6M8C7u19aQmhRHalIfhg3uQ2sQZYkeVx1/4LcnWj25pOxDfpk3yzlmgoiAAIKEIIgIgkh9uJbk5ABb12aC6Udy9WvRtyCLSPvjkg7UiWoJO0pSFm3by9xiE/ZLMto2CALTJA/TTcdyohgWtiTgj5iAIxa4cKvj1ksVMll+6hNMu7gnm2sbue9Pze8xD44aSEaSm5KybfiSO1A4/x+MWvfGgfkFNYX0TWtZWR0gNqYrdcomkocMa3X+R7eewsSCIO9PFPlpiAiNnwCfcPzbN9OXrghJctM24cTP1MfU8lXwW4Zb/enTrT//WOpY6Aan1ZOhz6Vek0kyojFJab0Q2rkPHcX/Bo6SlD8KgvC7SIoRdCwaC/zxzHl/C/3l5bxoZKG2USS+AVjSwRtVfRUQJJGIrfPN1gpsWybWdWRWHDPimMsld9tWEisQQFTbf7uJGB569DQYfuM1rc7f9tkXzJ0HgtT222vEsNrNZGkorOILdQnDSvvRvZX5jgy+dNgU5ZxPCtBiY+l7ZtuZHYfDAx/+nbf0T1h92coD/1mWRSAcJBAK0RgKEgyFCYZDhIIhCqp1FLeOGXE+VsRkYNE6+oSKWJtyKoYtghlBMHU2eTcRVjUiVgwBApiGiWmbmJjUUUeimYiroumBL8syHfJyOKFTEbMKmgehJnib7+9IfT1K9KGgHHc8rFtNZNcu7GQvKXsLeOjy8zFdbmxFxVZdCKqKoLrQRQW7vBJVVBk29Ux++OuxvPjFAqbvzmab+CjdM+NYVZbEsDYyyWxaBsEqloGaMhdslQnJ1zPKW8hje3+gyIrgdnkYdf4rbe5/rc8U6p4ZROfymRhBP7L38Noj+yFEqxG5vWuIRDioWKAdjVm3EQSbhBSdhBRYt7Qc23AIDBzfXscHJ+QAYBkG4eoyrKjVLD0pHijl+DyRG6YM4PJ1e6lxibx8YncurWjgmrUFlPpk1FgFsc4hJmryPOIb83jkuNMoDWgEvy5m9r1OZo2UfypCaCZ3Xubc2hekJ+F5MpsuQMSW8aGgIaFgIgiQ6EuIDswCU3M+hgaWTmDFdpRUH5ZpIUiO0GHN3r1UrVxJ1tixiFYedfF7uPiXeqaedAt0782WfVvou8WxWPU+Y3i0b2cnRIwwdy++myQSePjUJ5m+8UXe2DaL8YnxdI1Lo+v6X9jbfQRJSSNg2RtwybdHfAyP4r8XR0nKH4XfaUlJxwlOjTMDpLuDuAwBCYFN+ZA2tDNYNoLppC1XLC8jpTxEanHAidqwnQOrAv2JISt3DzddeM4RrdeI1sqRPG3Lu1vBEKLSPkkJWz5cXn/bDaL1UloIXxwEzbRR21lPgxKmXgwyr3Q5w0ITkBQF8V+IJ5FMC1z/XvCt3/DjOaQIoiiKxHpjiPW27To7GPIPc2A5DLj6DVCarBjGu4M4Rk3ikfNntljmjPfPwPba3Hfffc3HE4nwdZc03AVOzIKZ7MLoEc8V45sXzzNCTSm72Z07AhC/eSMnv/oSvy7tBqEgeiSMEQljhsNYWgQ7HCIcrCHsdZNYUc3WNx9jeWwW7sR0jq+sYmHSGLbsSyDBFJCs1smxJUgtDr0ZDCCIJnfEn8yFpzixWWs/XEol7Veo1huqqX5uIplSA6WufmT9DoICRCsWw9Dhr5GR3Xoxw/LCXWwsdAiJbUJ8ei8u+fuf2u3Xpp5I9iKKFu5E9LrwxeVTXjiLMvkjhMYRWHZXbvx0HaAyu9ji8ohGvEelxjIoCTYwo3Qe9ZIPSMHoFIu6uhpB1MhP2El2SoTJY/IIhnTe+br4wDp3d5jM3zM64sMhdEurd9FnzN1s+nU2d+hXUY9zLvYTdjHTdS/Zb41pfew2RKqyAIMd/ftjIWBJIrJpYkgSz5x1JuT15qe83pzz6We8s+BZNjTEIyJSnjKGC6tOYvEzX2ELNm5dJZMkHtj7BLvjSkmvlzj7x8nU2QL9PAZjEhPoXLYH2bTpuHkJsITQ0Cvw+FKO/CAexX8tjpKUPwhGg44crEJ/fBy2qILoAlHFlpwpkguk6G/JRXr1GixbYMF9p+KJjaN2yx4C7xWR1+DHVbwbQXZULQVZIufYFFJ6t6z9sW/9bIyPPVw/Np04t++IZN/1vXsAEM22LRxWKIzYzkPd1HUM243LE2qzjRUNEi2c+S1dp53WapuICQmetklHyHCUUg0sHn3Sqczqs9389f47EH5HGXfZBsH9b1ZBNhrxiIfXzmgPQtiPJgioh6jVhrDxCK3v74gdIVZq+VAeOW8VuLxEhqUwuMEmKxtmygrxh7jWYrOyCEyahO/nn5Gvc4hBsHdf+nXtSr+uXdsc646GEBOXbEE0TB5t3EfjovlYNWXk5HXi80vGkpiUyud3LSE1vnVzvSXIiGJzAhMOOKntXlfT9oRtAxftE8jSNy8l295Dxbi/kzXp8HFXh8KMagNJ7ag9p+d3ZuP6QRC7mstfeJfE9Na1cA5GoONy/AnvUq4D9dFPdBW/2SLvR5rk3a8aEEO/TlmECwsIeRIZvHQHCB2Q7AZOiynh0ZHj2DK0lNyEJK776j0E4Jnnl+PZ2nigD0HwY9uxjO8whEjSE1SHQ7y73OLh8Tdw7VynHMfFA930j9Hpsv4biEBQisPX83gQZZBkEJxpsKGOhMFz2e4dx970AdQXbibWCtPHtgglNekjdd26htdPEFnXx0e8Eotpm2xKLWQJG4mXYhFtAc3UyCARn+5GMQWyKnWqvBDrtZiSALJ/D3GNTSUJGkSBtfGpjDvsHj6K/wUcJSl/EMKMBvMEZBmwIwimBnojgl0DloZgOx9sDQEdxdbQrC6oHueNWgw5D93Y6liobm7CD9p+rMc7tEjRM4w6wIP+sUoZS7GxsCUN9VSJjOETWx1n9YqtKEDBxX+mJLMzyAq2LGPLCraigKzQZ2sRssdkyRt/BklFkFRs2ZkKkoptyKTL/SlamYgib8eV4Cb3uLxm68mfPIGE1YuZPSeBlG6rSezbMqZEMwXUdh4coagV4ITRx9Gwr4ZFu1YREML84+Fnyc/swMlXOVVl2yNmtmEhA4Ln3yQpViNe6d8kKRE/QVFCPWS8Idtqk6RoloarlQDXKpdDDlYd24usuFhu+XYeolfl9b+/jyiKiKKEJIlIkoSY1YOh/AzAyj6D2D31KkoXFXL1yJbn1H50jfNwrMvDLDVC3+HjufCc5iUUivdEC1QaVqvk2BIlJLH5eRyIKhl7DqrcHbEMvGLbuj5bP3mSHv4FbPFMJC5nApXFJbi8blSPC8XlQpTkwxJzU3dIiiK3rzjsSpxAxFjN2y/MQZeIyufb0Skg2vvV9EGA7KRS0rw+Jh23HH/pVhord7C9+j6+KZrIzF0nkiXWU2o5FssLjumF16WQHNzIvkg5J3YYx6kZHRiX1gd3VGV4TL7j1IyEDAK6hr3dDwg0eAVOv64/9YLFL//cTP8VfraOz2LR6lKglLsMiynHdOTHX4uYvlfgoVunYI0fBk/nUyh3os+Zb7XY1n/+43lqu+YSsiVcNBLu2oX8gWMZONWxvOy3Ny0vXM4TC67gzi7XcsHI1uUNyqqKqBtzAkMGxXPlamdf1z32J0aecT0Anz77JN8uW8ig5PPoGZ/P7IqlxKb3aLWvo/jfw1GS8gfBv9GFqd9Azm2tFxILVNbSsLMU0SUjqgoV32/CW+0lP3rjjBuQjadLEroWcQr66SaWYbD706Uk16e1+jDJ6j+NSmMhZjCEbZgYy0xkfxJadUWb49ww4RhqN0TIjVMRLRPBMBDDIUTDj2AaiKaOKUkkZAboUPYjsq0jYyBjoNgGCgYRK4OcmGhw3ionYHfLgj2YPsVx78gCYoKL0ZPj+X6GyL7Nxa2SlIgt4WqHpASCTvxMTq+OZB83mrGBY5n36SwKSnezqnQjax/cTIzg4eq/XIcvoXWXixVyHpSS71+XxAcI2I34xCNz67QFUWskKCskHPJ/sWg1S0U/GBoabqm55cWymtyKrmg8jsvyYgkChVqYZCOEbVvY2NGpRWjceMYtmM8Pw8cwTwiBFkJfXMSfxjSXr9+P0sYwP2oh0k2B/iktLTnl1Y6LpvKXMl7+pQxTBEsESxSwRPDKMYR+m8G88a9iyjKWJFOUHIDJcPvuF3jwo39iYxMkyGR/kOInshFwtEKEqGaIxzLpoTsB5gs3NOJf85dWRro/+FVGEGUEUUIQJOe7ICGIElY0bdk02ycz9e4K3I1gxYcQTTe2bTeXxbcEx6Mb/S8iBtCxkCQ3CbkDSMgdQHLdOJ5Y/RsAr1w6FM1U2Lmvjo6ZjmXGbdQQ65/HS6c/0uoYlpUtI6T4MYIWY2/tz/DOyc1IWO/7R/D2De8SXhIClxMbUlQZYE9VEBu4YZLzn+hLYrV3DLLdeiHR3jGN/FYN3vROZKSlcMFJ44nxtCTDmTEpeEWbJ7Y/x9cl/+TLMxe3aBOJWsh6nH4p5WeE0d76iNy//YNvX3sNqX9vdu+ziYlYbLZGsq3GxBKGkuA/fCr/Ufxv4ChJ+YMgJ7oxa9suhFf4wnwSjBT2Gz3jSSBoN8V0CKKAGudFpbn5fLdHRK9r/UYjShLpw8Yf+F2w+VPwg+RqO7W0wSXzzJDz2fXYiUi/w2XSbEzbKnC9s43yybl07ZzMnnc2IhoWsl9z0nstG1d5kPW6Y+6vq20Zu2LbNrVWDBoys9fOpqKhgtOGn8aeij3kpuayqXgTC35dADSJRrl9Hk644gyMkMa7z76JLUJJpJLv353OmX+6CPGQIIjGfdXUrXbcW1tXr2LV+hmYRoTk7A70nTiCvN5tuzsORYgA1XYDr//6Oqf2P5XMhMNr0hwKb7AWS2x+SQZ152H/Tng3N1tWixIBYTNMsK4pZuOXXbt5bWcxKDFc7bZI9jrny2m9u/DOnj2s7j+Cn08e0OYYOv2ynXnRGJDXGur4+MuVhFSBTl4Xr4zqSobXOXfWVjViKyI3ZLfu9tBTXfw00Etnl0qfZB+mYWGaNqZhYZk2roJdNCZnE7ITwdBBNxDtemAJHslLssdJo02INNLPjKPMrSKI0Ywbp+IfhqSwbEsWRn0HQqk55HWPJTHWgyKKGBENLRLBiETQNQ09rGFoOrZpYFkGtmVgmSa2ZaCFg1h0p6HSIrkdL47otqERrr9pGj5XQtsNo/h88Q9ogebXpichnYfP6M45b+3mtLe3I6W6yE70UqLu4q+jOuHXC0nwh1izaykDO49otqxmalw5+0pwg1JbxaK7L2Wx6CImuTN5fQZy/JVn88kdD6IH1qFL/Q6QlOo9fmwBOnZN5PoBTRZNwdKx2ghcr6yup5Pb4OLrL2j2f1lZGXPnzsXtdlNaWkpEK+Cv/cM8VOZhW8BPWPPjVmOxLItl+5YxPGM4kYBjVZN9MYw/8VqsM/7Mt/dfgm/1dhpWlSCnxtF/bwPrMsAUJJCzcW9PgpbvLUfxP4ijJOUPgqtrInp5oM35oiFSSSnpp/bG0g3MiEFSdlNqoGVZFH89HUvTET1uRI8XyeelrLqMZWox8hPbEQWR7t26MfL01osDJkzuRvCdAKUl37Hhp0/w+bojiSpi9CNJKuVVFchi33+ZoABoEQsX4I5RicuLpff9I5vN37e6AuPzbWRvd1GIyUu1y/Fs8dAtdwzFe5fg86ZhaE68yYq9Oux13s6e+cXRdNAlHcVssnz4DomzkD0qV959AwA/f/g9C3eu4Of3v+O4y04FoGzxRupmFBIrJDSNac93hHAUQisLV7Bn19tk9e5J96FnUFu9HtOI4I1No8/Qc1GUlm6dvW6nAuxLRS/xesHrPDzwYU4a8PvqjGTPK6N2h49dX/dEVEREVURQJZ6QIuz2yrz0U2+GTTuf4dPuBcAwDEzRRGvUePmVV1gUn86ahHQaXF4yI0FO6d5kBXlg8x6IhYx2ChQCPDKhKxeVNjBhawHVHpGEOoOIbrPYKzJ53iZWTxmAJIk8t6UEQbY4uUPrgn46sLybm6npqVzRK7uVFn1b/LOsZh98exxXDr6Pq3se2b57+Vqn+J1sQMU6KBU1hEE1XHPRWbhcR2YdK9yyhh9eqOVwchz76+VI4pHdNi3bwG4lG697bgf0AfUIfpO4oEHJXj8vb6/FJYr0XLaHnMosfln0CHW3XkWXvD5oegQtEsY0dCalHcOa3WtJqFYAHduKoAUDbJr3Ebs3rKWxahM9xpxDoeYmobyRM08bwg8byvj7yX0YkdU8GF60dCyl9ZihsoBA3wznPLcsi+3bt5OcnMwbb7zhWJCi8Pls4iWbvh6RDSGLSZ+Pon9CFssrqslApU+nsVzoHe/UIfI5FjdRFJn68AdU3zyZd0udY+QL19F155fszT4GObECl5oHDDii/XwU/904SlL+H4EZ0YmTE6n5Zjs6EdLO7NssGHbfgl8J3X1fi+UahoymolMOHaQcyhor0bZuYiStkxRPdkeCbAIxgKIsJRxagSCazW7OgfBEFOHf8weHgwaxgNvb+kMiY1Aav/2yAM3U2dbjNdbLNdy79FfKl1qEDiJHI70jyQpmtVhezVGxTZtGrRFvhRdFafthNOnCk9D+GWFx0WoGl4wkPiMRbUYViq0QGmThzUymvKCWqVf9nczOGaxe8CylpR/gSWsAitlb8xOWpWDbKqFQkHlzXyMv6z569G+KwaitLCJRsuisSNwz/mv+9OOfuGvtXSwtWcoDJzyAdISy/TsaU0hwaSQN6o4VjmCFNaywRt+Im7zyWsRykbqCz4iceAsujw9ZljEEA9mSqaqspFtFBSneIrb0GsSck5ufA9WGyeg6iY9Ob107ZD8EQaBHdjylWQNYtLqMdZ9tJSzYGFd35rm6Ok6au5EJqXFslEy66yJZMS21eQAiUR2Q31NXqUFzLDg+5cj0WQAu+/soduzZjS1YhIIRVi3ZgbUqlRd2T+e2h845IiEwQ3eCT8P124nUtL49pm0S0WpxwUHaN+3DxsRqhaTcu30HZrqPK7rv5dGBJ6OZFt0f+olnZ2zmT5We6LI2q//+JqtxkqEbew5Bqd5HbsVecknAFCPUxiUxqv8JjD3/RN647kIaqzahuNOZdPlZzHx5BrIA94zqzD2jOrc6PtHWMaWW106gZh8NtpfM7Ew2bNjA9OnTW7SZMGECHo+H5ORGinZ/z8NDnifkFfnn6qfRNmZzctB5McnsWIQe9Dsk5aAsN7u+lOT4pdwSB6trsrATYJ2kMbL33UR62IT1QcDUI9rPR/HfjaMk5Y9CK7pOlmVhmgaKotKQ5ydYEyDOl0JCXSr+rftgWNMDxfBHTaY3/4X4Pr2xgiHMYADzux/wRkJc9NCVvP/kG5htiEcBGJrzsLTsExg54gW83iRs28Y0dQwjhK6HmLFjKWLbXRwRIiEnOM7TXpyHBaZq8eJlPzNj/t+4f88PZJk2Dw2+A01rZGvJIjp2744vaQxff/M17mD04ZEH9192PwDPfPEMtVW1ba8jirGnTWL5Pzbww8cz8AoycbKbziM6kn+KY07PHgcFG2cxa8YtqLEasjcGKTSUfoOfoDFQRnaHIUiyQvH2zWzeeC8l1TdR9PXbpCSdRFxiHkW7n+P+rDAip9M5vTNfnvsld828i2+qviFhTgK3Tr71iPZbvSlR37sTPV/5ptX58995lPQnP6Rg1c/0HHMqmq5hiiZD+w/lpkk38eHy1Wyd9R0jV/7KHbu3cf155/BxyW98uvFlIrioD5zPkb6dCoLAqIEZzJO3kGQIXN+/A2t/DTJP1VhbX4fHtHlqYMuMsv2IRONi1MOV/z4IAd0JhPbJrROF1uD1uenfo0klp0/3Lrx2z2x8lWlU1taQlnT40g164x4gg10lr1ES3NNmuzjAsI/ckoJltmpJmZKWzhfVlWwKONeJKom8fMkQ5hVVU/jLcNJLNjNs2snE/6yR6urAB27HraknpjLygimsee4tnr36QQCS8zM4MSmeCx57iUB9kNzenVFdKrppIbWhqbQfkqWjiS3dPfN/cvRJvlxZBisdgpKbm8uePU37pqqqimnTprFrl2PJEgWJPhnjeHLSCB5f/viBdp3thejBcbgBNSYO2zSdOkRVu1Bw5KMGJztp8tVJewnrZyIIX1Bb3pr17Sj+F3GUpPxBsG0bf9jvSHIXbeaKBVcTEIN4LQ+nxExm3PjxDB84gZL7Xya8uRB/YRVrVs9AkBUERUHbvJkYIK5Pb9JGNwXfVq9ZRwOw4bvv8GuNxIoeGpdtRlBlRJeK4FKxVQniYzGi5CE5rTderyOnLwgCsqwiyypudzya6UIRwv/WtmrRYFRvTNskRdEl/D4LJJmpk57i+MDtqGoMkuI8oEYNue5A255/6snu6t30ye7DrrXzKV4+j9yh49E0DTuqllVTUEh8bg5SK1aVmJR4pgwcz89rFqIJBi5JZOJJZzdrU1hxI2osKJExHHPia8iy80abRMcDbfK69SKnyxdsWvkZZZEXqDcfp74KLDKQAJcnzdlul5cXznqBMz48g+kl00lfkc74zuO548c72BnaSZKYxB0j7mB8z/HN90nYwIhpO0Moq+9wTD4kfN0dvPjPnayp2uDs77AT93DhsEHs7daF5z/8iJjKfTz2z4dYnD6X2NgB4K+hUnqK6YtjmTbqxDbXcTBEUSR1QDLmyho++Wwz75zWnR2BMH7NYHh6HNJhNG4AXO20ORQBwyEpMb/DknIwFq9dw4LvNxIbzMYzsY7UxMOXjABIcnUH6umqjCcpufXg5z9teQtLELhr1OPI7QgQHgwbA7sVq8uwhASgkqV6PsvKtzA8vScndkplSmYiH63oTNeYLHJ+ywMXbBfLDiw3rldPJo44jbEfnszTv20EIDPq0sro3KHZOnTT5nCFvSVbR2glo8lorHb6zEinqqoaRVG44oor2LBhA7W1tQwcOJDYWMd1Y9lOFJ0QVTt+6qmnDvTTseMaKiMinq0FbOjbh5LXP8IfOxMrKtDotq9mqZbDA8p7DBB38WrmdZz0XRGj9r5ABzUevTyAkv7vZcwdxX8+jpKUPwg/hOfyWPfn6P1eV/LcuTRIjok5yYrn49BXfLz+K+7cdQ2DPnsZQfWhosMmE9G2ESybGNvGkES8uc1Teaui0+krHaXT1EgcdV9XtzsWQWnb/fBjsQWoDLr3G8KouBQZSXTkrupNi5QBKbjdMoogECxpJC0CPllClZ0Cfm5FQi1soBcGCQt30UMtJD9eBZcHQXWD6kZweVDDdVRGiqn2lyIqHnyuOCS59Zt/nCeOvjl9KdyylMh516MD84d0xO7ZG1WPYeHbb/NzcTGpjY2cf9VVJHbvjm3bWJZ1oLT78NOOYdDkkbz2/NMEQ6E2U2tD2hY2Ln2T/qP/1KqrQBRF+g47j97WOVTt204kFCIuOZ2Vq8eiKnHN2vZN6suOyh08uflJntz8JABDfENYGVnJfUvvY0HPBc3au8ImVkzbGUK7584gB3Dr8M7GtzEkAdmWKVtbxl277nICg20YPnAwiIPZs+RDAO7sdjbHdBzPlZ/dwEPb7mJP9V5uOuXqNtdzMLJzYylcWU3tr+W8Xx9kZFeTnIxkpKwEZ0xVASzbJj81hn31IUBAliAcVVRVfoe7JxDVvYltI07icFj47WZiS7IJdCjjhrMvOPwCUQg1u4EEEhOGkdK/dQJ38ey/0zcSodx8i9X2W9Hs4/0UxMa0DHZr9XwXF4c3pTuiIJIT2kuW0tIsGaeonOApYFaoE+dsruNHZRc9kjqze34xE2pzwQc1rjLmJ89GknRiwzH4/alMPPd8ABRFpnR8/wN2EsOyMbExbTBtm0gkTI1mE0SloC6IbNvEmjqy7KSc24KIIAhItoEoSo7CrCg5GWSCgL+hngxZ49prH2g27r59W8YR7Y/TEQQJ+aB4J7e7ni7fFJG4VWZj92VsGTiavEiE/pmZKKJEdTDAB0XJrPV24jTtYWII8s4jj7Cuey8mnHQMAOds2M3zaT2P1u75H8dRkvIHwZ9pQAVslwrZZOwAYP5JP5OckoalmTz41d/4qOA1zPwMLEFg+DkXYssylevXkdy1Oz2vvBrLslo8XO/+29/w796NpWmEGgLERhRE08KOGNi6ga0ZVL7yGg3DR+AbMQpBEOg8qlub47xkSBrvraxA0y0igkWsW8S0baobnbf1UkxkA0zLRthWSwlRxf/WXETL9tBNKGO2644WswZKMLABzvvkIza2km0k4txIExvh5q90PIILwbIP1L/NWFmE3sWR3f65uBhZ16l3u3nx449xaRqaooBtMyYtnb7HHcuedeuQVRW/348ZCWOZJuJBkvvHjN3IqoWP0Bj7KdXaC0SCF+H2JbYY14HxiSJpWU7sTkXlVgBUtSkw8eOlH/NV5VfIlnOJGVFNkJURh0y6RR8zl84mxu3D6/LgdXlJrreoDBoYoSCiy93sWJcVbCD9g7nsGNuRbrfdx4dJceTH5xMIBfj4p48pKy3D0ixEv8iGjWt56JozOG3IQCa8P4FP1n3MqX1O4aNL3uLPn9zOWzX/4JiCUQzs1H58CsApkzvTOCaP925diLJoO4GXn+Mvo69mcMwb+PK68m6pc0TiPTL1oSbdE2+6FwYk4vod7p6g/u9ZUhBt/Gn7uPOuIycogCMhCwdqBbWGEwJOvIxUuJRotjFN4vkgInBxdhqEGmGP4754PtdZ5sefnZiQ/deIbStMldys5EkqhXTGr/MDa0nFYhZOPErlMXfR+6D1L1l8Ns+feyKaIDsupEMe3PsvPwGQTYNN+Y4lcuIT89rcplmqzYB9X8IjXx74z7QEdoo3AS5M0zxA8tuCbe0nKSKqqnJBeCy/KpvpXz+ISGAxxVO64dseBNvm0kceOXDNhaqqyJwwmVvG3UhJbBrXrfyaB86JY9XImwEYZMt8Jml0La7gxg7p7Y7hKP67cZSk/EEwBZNUTyp3d76dN9a8ztjk0SSnOO4BUZW458yH+dufVlAZ57xF/vDDV00LF20jIb8TOZOObdGvoqoktaMMauo6++69FiVtKD1PG9pmu/148MyhPHgmTgVdGyoN40Dqrq2IbDt5CB5JZH11I6cuKufPp/filuFNsQmWZREIGmiNEZ789EvWVcoYFy3BjoRAC2NrzrSivph98l4KSmcw2p1JavZwImbkwEezNDQjwojv19B1r03BqDRsYI+rBi05li6338dx69azYcs+gsEgvbduY8Tbb7Huq6/wB0O4BSitruG3inJ2vP02ZdFUXFQ3SsDPji8+pfu5TQ8zWfEwfOKj/Pj5KpSUHbi8CYfdV/tRUeHIkhcWvkYkUs3QoTfSEC0v0NfVl72RvdRYNXi0eBrdjmBZGXu5e9tBsSq2zedA6qwV7Jg1GAtHUwRBQBYl0HUUIObmG8jvNIiAHqA2Uotu64weO5q9/r3sqNtB2dwyvKKzrWd/fDb1cj2D3YOdTZcVLh5wIYtWz6Ux3Ham2aGI8SnUuwAyeWXioxR4DArIhtKmNvUhA1GAi0d25NMVxfi1KGH5HTU1Q1F3T+y/SFJ+Z6HxJiTkAQGIaedhGJ8LET+Zd+5us8nrmz/noZVPUWJHuCNlFEmZ2ZRV/0rE3Ruw2VFTzG7/Njqrp5Ibl8F9bCJsr2U1nQmLvRle/Diz0jJIKO1G9+29CWdVkRDTiZJykQlxGt5xPfgm5GW5msOpqQmIOFxFAKfaOA5Zkr0+ptapBOR48vOSEZ/9Oz5dJ+200zCtaCivbbFbu5pUeTcewQTLdMiaZdC/uo51ZgJfPPooZ99zT5tWRwA7eoD3t/GgMkUfwJqgQX7nRMY8/wUfP/c81Nfxyu23sy47nyXd+iGZJnF/vRvVNOmRXI41ohyP1BQk/9nYnryyp4JHCsqIVyQuzEw+alH5H8VRkvIHwbRMJFFi0uApTBo8pdm89z56jL2LVpBR48Xf0c2JaUMpWL2C6mAj2Snp7Gqo5rM3noc3nkexbAYNGMqYux84ovVGGh23Uu3yFVRs2EBaKybb1pCUr1JfazMgP5mIYRLWLAbmJeCJEpaakGNZiXc1P4VEUSQ2RoUYFcs2iZFN5M4tC/flAGmN5QSnf8vkjBGcPuahFm1WXnQ6vnU2e/O8nPDm980sHwD0PYbRwM7jjsesqSE+M5NxN9xwYHb1jh28/P77lHm9+MJhLr/ySgoXL2T+96tAGN3qdttqKXpNzu+6IXq9Gfj9ScTG7qXB/xxwI1eNu4pdM3extHYpCUICU+rOw7e1M1Pv6k3Zqgp+/aUAjSqCoc8wJRtDtFg7JJ7+3fthKypWKMiOhfMRLZvEQAgjRuf7YSJzVtwFK+5qcywnacejlJgs/+gjdrKTLCOLJ0554sD8QLSAZE1jNXVVpaguL4rqQlJc7T6MTrmuH7O/28nesqZA5TN7w9i+A1i/p47NZX6Gd0ripmO7kZ/i4+UNeykGfOqR32KCUXdP3L9sSfl9hcYP4EiIzRHU3hrV62xm9TyLfu/3o6KxhAt7vs7B9Z8/WjefD8r+xFljL2J8p6br8KL9X8a9D7bN5jW/0Wvmr6zp9DLdJl7IwXbPdYtn8FYom1mThrQ7loNtSbPvLyA8YCCnXnZkad2nA2mvvcacffuY8eSTnHbnnW1eD6bhCLVt234e27aDZ9JUEkKZdLn9bey+KYQXf8/2+joAqmJjKU5IRZNkhihgV1VQnyNxa+wTbKEXvwlNpD1Wkbg9P4NKzeC2bXt5vqic41Pi+VNeGlnuo0Jv/0s4SlL+IBiWgdSKtHlp1W6qZi7GDeRcMIVzTr0RgIOLo6/6x3PM/82RLddFgVVrVrDr8s+JU4L0HN+RHueNb3O9pmVhSBLpa9dSce55JK9bi3QYrQwAJddNn44C0ycPbnV+bdgJwk10tx0cG9QsvFLbN/baeidbINmX1up8K+w8tCZ9t6QlQTm4XSiE0IrLKLlrV07u25etGzYy7qyzSO7ShdCe3QjYuBISWu1L9gZxm60TmLbQKb8fnfJXMH/+bYQjswCQRImnT3v6QJt3751PAItvnlyDYKl0TUph3LUT+OKhRfjrK8jPGchprzx4gCiUF+5i9ta10aUTGbqrlAm9jiOndzpVoSo8sgeX7EISJBJdiWT4MuiV3IuvH3iG+vhYvtuxDfLhZHUwHrXpob+/ptE9W+7ghBKNyfFNLhrLVLBNBdtSwJIZvK0MuTERCzeZgspFosoFSSqbQia6IDFk4sukZGczdUBTJsb6H9+ie+FyRsX2pZgxPL35F7ruNZFlBUkQUASiUxFZFFEEATn6fVW54wqL+R3ZPaG6akK1ldiiiKjpCJqHxtJqDElEdClIkiP/L0oikiwiiuK/9kYuSkfEgARBIMmy8aotVXgNMxqnI7V9/emmhfuHPzs/TL3FfAkw21AfbhO2HRXBO3KMvvZaIs8/z4K6OtzPPssJt7aeodZQtbXZ75A0gw47G8k+N0ykoQLX7PO5FR+vN55HvKKSJorIdTV8fN7JMHkc327/BPZC2N0fIjDJvY9nBjolOwRB4OnuOZyRnsiPVfVML6/lm/Ja3uqTz6jEf0/d+Sj+c3CUpPxBMG0TuZXUxdq6qET9oJwDBOVQDP7TzQy45gZs0+S1y69GV1JoVFMImhqlv1osnf0Z2Xkqo26YiIDFF3f/go6KZOuIWAjD/oYQDiJaBovumY0gCWR0ieXUy1qvgAoQEkR87dwLq6MkJdnTNkkJGOCT235NjYScsF+3u/UsDO3iqXDbC5SuX0rOkLbLjdmRCGIbAaeDzj6bQWc3ZfKE6xxLgLuNzA8z5CPsnUPBli/p1PPMNtfZ6rJmI5bVuprvWbcPZ/orCyiv2Efv3Bwm3zASSRG5+p9vs/ajGfw8801+eeQlOo4YzMoFMyjZsbnZ8ivzM5n83Exq7/kzL24cDYKBIOp4PQE6pcOIfAHNX8ngZctxaT529ZsK+YDRfDsn9B/Fi9Lr3L/henQhi2TPVVhmBNOMYBHBJIIlRJD1HaT4t1EV2w2NLARLA1NDsDS6SH46CBupeGsK4Ts24o4SxM2Lv6Pf0lsAWCMIwBhWC+lsawxgImMhYSBGpxIWIuZBt6CEum0ogNzOQ/xgLP7kOXyPvIEalWnu2uUs9uaM572H1rXa3jIq0fyfACaOc2R/bIeIJFgIny7F+jqMw+MOjvsQwNJpNJNYdesdlJu9HTeL4NTqESWB4ecOJWtQTyob9lIjCmTF5rRYf1G1o6x8448P8f7UJ+ib0aFFm5rqKjoZBQD0HH92i/kiArYgHlGx0P1QNI1wOyrTbWHiTTcRefJJlvn9uF9+mQlRK+VPH79P6Y5tAIT9VUh2LmMvuIsOXQfz6TO38Io+gD9LX5Nq72NdUQYjfGXc+tIzhINBnvllNf38tZhmiJlLziROc0hOesY02A0JUiOZB1lKBEFgVGIMoxJjuLljOldtLOKC9bv4cUh3uvuOnMwexX8ujpKUPwh2G28zwaDjjuk/9rh2l5fU6IXr8eGRFKZMSyJvQl82fziP4jURCkp8FPxtIS4rREBNJl2sIM4TwbIETFtA9yo04kJUBLRKF3uqNUonVpDVoXUrRkQQiZHaJhh1YecNPKUdS0rAEElpJ1HjvpVOuqLLndDqfLPeMSWr6RltdwLYuo7oOTIXQSTap6cN/YxhI6azYtUpFJTcQUnRL4w94ZUj6hfaJym+WA/TrhzJ0/94lqwePZGUJgbY//xTqSwqYsP6uazbNBtJkMno3A1j7VqqYp0daIsCdT430+dpkApjOjeixkgUVFps3etiwy6LK7Z9SVI/x7UTEZ3YlzfE97jBvOVAXJEkSUzoP4qHNkFcTBoDRp7f6nj9m96GNTPwnnUnKXktz81FL1/D6MpPWfbsVIz8iUiywoiNDwBQdfUarsvqxHXheub9NpAu8aeTO/Tvra7HsiwM20AzDc6avpvOaV2OYE9DpKGW6ulfoLhF6u6+BskSSIyIZIQbaFy8GnYVIvzlL9iWjWXaWJZN2c7tVG8wyB9yKi63F8sysU0LyzRQataS7ArS6OtKTEpaNEbDcuI0bAu0IK7K3aR6SrFje2NbNrZlYZsWW0vzKV23i6xBPQlWO0HxkjelxZiHZPXis1054NnI/MINrZKU+DjHArNaGUjfVqyH+7UOLThMbegmeEJBGnz/Wirv5L/+Ff+dd7GkYg2rzz2lWdCP4HH6FF155HUdhTc2lqf0aZxV7+NVpTdbFZMrurzAV+u7I553GnNHHE99/9Hcue0RpIcvYnSyyuYesZiSwG27nQ2bHujCpgXf83DXHMZm9m82lkRF5v1+nZiycjvXbipi1uBuuH9HivtR/GfiKEn5A9Ham08w6LxdJSYcvvQ7gGXqeFMT6HjcAAD6XHosfS6F+m27WfnRCrTGCIpdyDEPnYsS2/qN6dVbf8QKqFjtmK81UToMSXEsKSnetv3DAUPEp7Z9E6mxnLgWTxsKngf0FOT23wJtw0D0HVnaaqTBCWh1p7R8iAAkpXehf5932bD1AiLKT0fU535YdhDstslSOOC4r1yHFGoTBIHj7v4LY+svZ/Nj3yHaAv0fOZu1/fpR53Xhm3YG4bpqjD6duMYs5PpwF/Zttnjz+mPI75LDpu3VfPXBRtzpDpnISWvg2FuP551vHMEvzdBxS846y/0FvL7iXvyGidKe3kfIsTgJ3tZJbMqQM2DWpwyPLEHfshxFcMwZa9zDGZjlyPHbehBLFJDaqQ4tiiIqKqqkYtg2cYc51gDrLjsXdck6ugAFx3bnpDP+3Gz+nJWzSa1Zz4DTm8dtzPuqmOoNMO7CU0jJbF5bqWHXavjgQwIDryXm2CtbXW/k/i7E5aTT69qLm/60LApv+A6zYiW1c2dxcomTVr5VK6J+zaPIkowkKE7wsw2Tuwxnbsle9m2z+TFYiCyLyIqEqooosjOtlyYzOLwc45EuaNctwuX1ISkKgqwgRQNVzYPS6w8HVYugHAGJtyyLPduK2LVwOfWr16Hu2krGviL66GH29G+uWnvi7ffTc3DLQPxnzx9M4UvbyTBFhkUkcseWkZdxA/PmfMvwtQvxpeUyuHEpAGnVGoE9QQo6+nhAeIyv5Suo1GGrmc1ZW21eCMzjnC7NlZO9kshrvTswccU2/vTrMt6cOLLFGI7ivwtHScofBLuN6LxwyAlk9HnjW51/KCxTQ27FdBvfvQOTHmr5ZtYa1FgTQ9yD7E6jqqoRWXYjy25U1YMkuZAkGU2S8LUTT+KPOJaUWFfbp1DQkvHut8W3gju7nc81m17F52nd9RIX4/y/efF3jJh2Q6ttADBNRN+R+agjAT/YNkpc2/s7I2842zccT1ice0R97odtB0Fo2wQdCTokRW2lmiyAOz4Wl9uHaWgIokjcHbchPfEUxufT6f322yT3HwDAN/NXcN3XtZz2ymLu7NmBmlUBVMlG7BjDsCkdGTrAsTxdm3YLr1U8y/KtaxnX30nXnr39Xb7Ys55MVWZIZtvuPltzLHyiGtfq/O7DJ8NwxyqlALZlYpomA5Um4mNqdQBI8pG9xe/TLb4t3cSGT25EllRUUUUVXahSdCqruCUX56xeD0DZYzcw5bTrWvQTDgawW3khsCwzOp6WD3dbd46N0IpM/H4IWHAooRYdl5W7ahfuRd9Dx1wA3t39W5v9yKZK6kqZXUsL22hxLRu41vn64K6D1m+yqUM8jADDtFCPgKRELAtV11E97btG9hXuYeeZ55AcqCUTcPkSqM3twt4pZxLTuxfHDejFV2+/gbtiF8Ovu6tVggJwQq9sXmE7AOmd5xMbE2DQqdfw1u5iOuzcyOfnnwp2HRTMxyheQnHuMCi+Bg9BfhozGYCB82dTZqex09+yWrtlWWze66SV/WjK7N69mw4djuy+dxT/mThKUv4gWLaF2IrFYN8qx3/+xSN3Yye4ScjvwJ9vaN00DmAaYcq2LWLG3xOQVBVZUVFUBdnlQnGpKC4XaflZdOjTttk8Ie9bEjrOYdPm1ueHTRcfLXuTlIjNmrm/oUkChiRgyAKGLGLJAmOrGzhLXM/GVz/Glt3Ykgtkt/NRXAiym9F2GeYuPzu+CiG7vMhuD5LbS73HJj4lg6o6x/duqz5CwQYU1YN8kAJmn2PP4Ycun5D+xBf88M03uIcPYeKNjzcbq22aYNuIsS0DFVuDFgigWHa7mSwAphnCPmKD+v7BBBGE1i0PAFpwvyWl7QeGYACK84DtfNEllHftRum111F4zTXEzpmLteYnus1+iDmeCtYpnVi76hEAhg9IJSXBjVwcYFfpVqTwVqp+k6A/3LriJp7i73TOS+CNLdPxiQI/nrOq/dTSaKaN2EoAaKvjFiXHWnAQTM2xErZnSWkNJYE9WOjY6FiCDujYggGCDoLBgmtFnn/DJPTaTITTrm+xfFZGBogbWvxvmftJSsvbnmU4lkGhDUFBcFJ9W6tCaAoS9aOm4Zr6Huuwoi4sHc0I8cmuhby5z6CEdEbIu+gTTidppkivi/qQl+pD00x03ULTnamhW85U00hq3EWKS8AyLCzDwjQtqkIOGTdajKJ1NIQiSJaFy90+Saks2EtyoJbdF1zHoIvOoGfHljE1nR5/mKdvuoXFrz9NnO9eCvbs47STJ6EoTfuzau9uwrXPkj2mjtTeZWz9Ip9VCy+j0/ZKTEnmvgcfIpiWiS1JdMgbRg9BRQJSEiYSDtfhdidQaTsvJ5d3m0ijplNaW0tefByyLNNvzjJq3M75NKxoC+8v/ZGJEycycuTIw17TR/GfiaMk5Q9Ea+6eQcdMYfaKjYhpsbh3+QmUb4V2jAainINlFLNzxQ9REarWblcyN3/yVZsXrSfWjxnqRHbHazDNCKapOVNLwzIjiKEQXRotNueBkJqErVvYugWGBbqFYFj0sCwSrcEE/dORLR3Z1lBsDRUN1dZR0RiumnziH87Mz8oPrLs6TuPbMWXNxrN16plkOyEUmAKYEhgS2IJMvuWjqMMU/BEF+dc4Plr1PrIiIQo2oggSJt0QMD1HZomKBINHdNJbZhiE33l5CGEE2n4gh4MRAFztBPyJpgBe5zzZsbuIz+uDWDffRr8PPqDLo91JcAfQDA8Nvh70tbaRGPcS8xpuZNOqqkN6iiOFOHqUj2Br+lL+vPYaUjYp1JsC13Yedtgbum06miWi/K9nUZi6Y2kRlSMjOuy6l3MH6Nx9RttibKZpEdQ15te8TpcPXqN8WwGZPQ8h5JbVuiVlP0lpJTDX1p1j0x5JAauliJptYlsSkqwgRgPjRRFk3Dy9eR4v13RmgGsPL3ZLY1T6eGYu28MeawedOsTTJat1K1UTWoouLlxUCFo9hnlkgjCBoCMopx5hzFbuMSNJa4WgAMT4vHSbMIV9M/7J/KfvBuCfe4o579LTufSTcwg2NjB5uaM1U/JbAiXWQLTEGsIl5cQhUhnfyO74jXRq9OEO+KkpLWZHQQE9eoC75mUWLX6ZUCgdw+vEgZ29tJC9lkBIVnBrhRiyjOH2MTZcxz/GDCVlbB/mzp3LnDlz+HnOXK6ZeinpA49aVf7bcJSk/EGwbKtVktJ36Hj6fjYegMeevhK2lrdoczBu/vC55v1aFnpYJxIKo4Ui/Pz25+zdNLf9h5DoRxFz6N279eyVxvKd1M0uY+AwmdQhvVtt0/D+NzRuFul694pW3y5t04SHkxg9uCepJ96CEfRjhPzMXPc930Zm8EDeeXjcMutD5WTX/ABA8RXHIykqlq5h6TpWWCfrm/X4AiUU55yJaElkyBVImFiWgGWBpoOATeiHGZRndkH0eBDdLkSPG8HjAstEcBvI8XFICQlE/A2IgohtWQjt7CPTDMMRZpnsh2A1Ur29gBe/uwJRlp0HlywjKyqSohDxh1FDDaz79Cu88bHIqoqkKsiqi6qiIhqqyunky6TKs5JNq3L4y+Y9bMzpDLmJCH97jLIFjn9++0Uf0KfvcVhaiG6P5qKL8WRd+DTxHePQgwZl/5hFXbCR9JM7wGfTSLLdLM6YT5Wuc1ZOL64f8/bhN8aMYAkgSr8/K2Q//P7NdCgO0rj3EYQdc0HxIKgxCGoceJMQvCks3edDi8miU2osfs2HJNa126ckicRKbhLiEwAItkYkLQtbbOWcjKqjtpbObhtOfFR7JMW2IhSt+4UvL7wYBAlRlHD5kjDCKdhWc0HFD7bP4uWaPM6P2cWzQ6cd+D8UdohSrPtfu/UeuNSOULQuEAojAi7v/5lMmNzcLHaLLlyWQ+qCi2bwQuHX7BpQweCihAPtbv3suwPfbdtm5pKveW/b/dhiOY9OfhRJSeatH+dQLnQmj0TclkVsbAc0bTE3Nz7DczF/ZYeoMihUyympaewMWmwNhDk9LZkr+w440PeUKVMo3lhAaWMFK79cwMhdw1FzYzHrIviGZyAnHM0A+k/HUZLyB2HGrpkE9Eb+uvxtfIoPVVRwSwouUcEtu/BIKjWNtSQJIqu3bUNVFFyKjEtx4XGpuBUFl6qiKEozsiOKIi6vC5fXeZhIioTQSmXTgyFIfgQhhUZ/BVVl28nuMATF1XQx64E6ABRP22/RVsSJm2iNoAAYwXoUQIpJxpPW9HZj7VkOZTCu/2WkJmVyIrDhmf7If30cd88eHHNy8xiDNbMmkJyfwnmPDUGWZRJjm1tM6rbupey0J8DQqXn9CQ6HcHYKgZR4nj3vVATbRpAtBFlAlGwECURRQJAgtf8+kro1MG9+XwRBQBCiep6CCEgIgvNdwKl5IggisiuMoljILjeWYaCHQ1iGgW0a2KaJbRi4LJstNfOxbBPT1lvEKu2JWU7v8TuZO09n42BHm+LPLovubpmfps4mb+FtdPn6fBo6bsFniAiCh24d4vD0cQKB1RiVBHeABI9AyoTx3DABPn7zY+LiNDIUm1Nj265cfDBsI4ItCIj/hsqnr76R9KIgEMTY/S2iZbeosH088IpxKjca5wLgdR2ZUJfV2IguSMQlt4xnEiwbqxUtES1qVWjN3XMgBkds28UnYCLKChldBmCZBnpEo7G2FCO0GS3goiJYxXsFq/ip1mKjkck41x6eGXx6sz7C+2O5PP+aINn+a9+22mcpm2qL2FxXQsWajUwEjPdeY4W2B0GWsGXJKQEgyQiyhKSolK7fQT6woXAbvtwEh2BLCpLqQpTdSLKCJAoMHDqQIe99hmjbzJq9gJkzfqRjYAsA6mAvx597Jxm5zYNsBUFg6qgzuGeHU728sLKQ4/t25dELzom2cOTsgsEgn36aRKi4mKlxCxg6oD9XnDS+3VRr27Ypbaygh6cDY4+dQOMvewiuLAcB/Av2EjMyi/jJHdqtV3YU/2/jKEn5g6CoGaDv5MctL0QD8FpisJZApj+eefe1Lpy0H6YoYUoyliRjyRKWJGNHPzG19Ui4ePu2hUiyiGXZnHBtX759cR1ayCAlN4aUkcWYFLNshRMZv3VrPikJZxKf2J2ufSegh/yAjOxt2xxtR0xEse2gWD1KUkR3c6IT1JxA4diYJrLR/dgzWR33NGl/fZHakVNJTG6Sxxb1CLbXS2pi6ynDJl5iT3sDpnQka3gaZiCEFQhhBcOYdQGq31uPoOzAN6gDZmMjQ2pryLM0DFOj4ZdfiYyOw9OrP6ZuYJkGpmFimQaC7UY06nDHidi25Zj1ox+wsG0Dy7Ki3/dXcYH0/FzOueHltg/ewfvQsrE0Az0c5reZT7Lu+7WkD3DcNul5exi0eyubcrrwoibzgFdh2sABrBf/hvvrcyj3V+Fa8hOq7Uc5sTmxs00J0d0kBDaq52RyA+swJD8VNdOx56yj23E/tj84M4L9b6qQxxA9fy6eidzpGGzbxjSCWKEqrGA5VqCc0g/uIN/j55+nxKObBuMOqQzdGoLVNeg//YBimwSmnsB22wbbxrZtgvjY3mEqke59WH7LD9iWgG3b1MhV/Jz/EdbYEN+/cwySICEhIuNM0/Varnep9P32fFiYiyZIvOQVMWLT8UpuvKKCJ16lW3IG5950y4GxBBpLee2Kq6mWa5m0bC21dhIDlGLuTd7KZT2ntrBohqsDuBJ3oyr/ijQuB14KDkdSzv3uPLAaya20mQj4NuyADc+32X4/dX27+nFGf7SPXKO5G9m0BXRkNBQCyNRJyXSO78o7ObN5Id8JwB7RYSh9+7cdjD02biwLGxaS7Gt+LVuWxbx581i0aBGWZZGbm8vt552H13v4jL2CtU6QrivRS9yYHHxD0im9dwGCpIJp0/hbCXKSm5hRWYfp6Sj+X8XvIimvvvoqr776KkVFRQD07t2b++67jxNOOAGAffv2cdtttzFnzhz8fj/du3fn7rvvZtq0ae30+r+BMf3+wYr6AD8M6kKjESFoRAiZOkFDI2hqhEyN4DFB3HVgWwK6bhAxNHRdP/AxolMz+t00dCzDwDSc/yxDRzPq8GmZIAg01jom2elPrjowjqo9jaSMBNv0kKDcgaiEqOVJ6o2nqa+EXTM6k1TdmyzOoGpFCTH5rft4bc1EaCdF2Wh0UlhFV/N4hKAWQLTArTbdgFS3F/HO6+FvL7L+y9c55poHD8wTjTC2t21/ut7oPIzVWBXR40L0uCAlAQCtrB4poRZ3z3iSL2mSBM8HCt67g6QvGpDjupN3zh24PEeWAt4e5vzUFUXMPHzDKARRQHIrSG4F0wwhuy3sUC5aIERqyh6ez0qnS7dB5P2ymrU1DrlTogTX+uwS8FdgCtlIed2b9WuZErKkHfjdccwFdOQC9iy8ijJtISXydoS5J9NhyAuoCc3fevfDNjWsfzcQMVowENUhqoIgICk+JMUHcc55pcuPkuzzMKxf2w+3DfNXUHP7rUimQX3fIeQt/4VMy8LweJEkyXluRyv77vUOpzqpDwlKKd5kCVGCxnCY5H055IhdobGE2NQUTMvAsE0M25kmU8uAiEZY9SGF6iiQbN5JSCQrtBcLgaBg05CayAW6yMEazFvKnODvV2s7UJmawjGJKmnuIewA7tlViSIISAKOwq4A9f4N9D7uA36Z8yLx6o14fAlU7VtPauYgUrM6geCULNy+6WPC4ULntw1g41K6YMRd6hyfdjiKZdvYVpD+HS7m2hPOwD4rRJxhOqTTMp3Co4aBbZjYpoGpa0QiQdbsnEuZsoDZ3nxG9fgrtqmDEXGUb40ImBEwNTA0rJ0/061mHghgiEVIlsBlQx5u93SIF+MRbIHuWd3ZuWkjm9etYc+evVQGwwfI1/nnn0+3bm0XQN0PLRhhxuufsam+gDQhgVMuPp3671fiXxhyCApOxpkgSvjnFx4lKf/B+F0kJScnhyeeeIKuXbti2zbvvfceU6dOZc2aNfTu3ZuLL76Yuro6Zs6cSUpKCh9//DFnn302K1euZODAgYdfwX8xLNtGAFRJIknykuT618rRHw6P/n0ZjUGDm+4dzcvX/tJifk6PRI4Ztx35oDTMtUu8lFe9g+wrQondhbnTqY4rrzbZ13ELGcN6tujH1hxXSWswA3Wsem8escEzSQiJHFy2bXtxLaNqpzFvyTImjHTSYneumUfs314kokDeiKYiipZlIZoRbE/b+0oPaAiAEtMyddSoqHO2I7F5MGuoejeRx2c6bT5cQcGH41D/fBydr3+xzfUcDpapIyoWsv37Mlksy+Ldu6bh3xdBUuHc27/hs89vJsU3k9J937Oj4AGSuJdvPCnkLlvLtd0GsTh7MqNKHA0XffKLLczhtqUgyMEW68od+yYxm99ge9HfKXZvIbjiYtLixzguK1FCEGTnI0qoe9ei6ibawu9AUUF2ISgqKCqC4gLVmQouF4LqAtXlxHOIUpMLUI8WMXS1E3zbZgntJhQ/+hgJpk5SoI7kpXPYNGkaA/58FV26tyTQs6Pn/AX/uPDAf4vXrGHN67XcMv6vDO7VeuXnravfgpm3UHX+J2TmjMYs+AGW3M3JSX05ffBfyMkZzpC3BhDyNiehlbrzQAxGXaO/1mmAdmj3TejaB6/9EK/pz9DoeozGIBAHFYEPqdhxUDsFJAVMv3ONSLHLMFhDrVYCwq3YdghoPd4iYBoIWOTHZjA2rXUS2ho6DhnJk9OPozo5m57jz223rWE8QCCioz6TTp4rDjPUyPZ9v9A9c1LLtqbG2pLZbA2uwhZsnnriUURcYNuolkGyz0d12Nlnc776krSrriYhuXUdo/0wIwab6h2CeOppp2LVNtDwSzWC4kV0l5F82TFoRaXUz2rklYYgu+/7hsduPp6cxP87992j+L+H30VSTjnllGa/H330UV599VWWLl1K7969Wbx4Ma+++irDhjmVZ+655x6ee+45Vq1a9T9PUmz4ndUz/jVYmoWgOgRk4OQ81s7ZQ0qOj1P/MhBZdYSjDsWAkRcCF2KZJnsLVrN+62rCDavoGjeYfb9uxl9cgaSoZE/oiyvOuRlbhogom1h62HmwHeTn3/Lhp2yuGAgMxPPLbrqcCqZu8Nmj3zNg35mEZD+b3wuw+b1fMAWTcIcVnALUXXcW/Q96ozZDGqJtQRuidABGwKkOrMa29PEblU52iZzSPI7FnZQHZ3TF/HUXUrVDtIzyyiPZvW0iHKoDQFEOl7FxyBj1ILVFOiASm+7sQ9t2TO2m9TVuN9zS+Bqfhf/MP4hjy4odHNvncT6MHcr5id0ZM/KUFn1aphtRrW3xP0Bir6sZ0uksFv06hCplH1WNX7babkKl43ZSf24706Y1OFRcxkYBIohAwyvHUiWno4kedNmLLnkxZC+W5GGEvpkva1J4+e3lKJKAR/OTVbeDRJ8LUZKRdZ3he7ZScsXNZB8zHE9KImd2ymtz/V2yI+wscRGpqsWVkujsj6hoYXuxDdUNzvbmvHsq9UUe7FUJ3NBd5OWT11O98F4eOG82NnYLyf4cbzqbgXu75jB5eH8cO4gTaWRYNoZtE7FsNMvCsG2e2/orM+rjmHjClxTvXEZCSjbLF12HEredGP5MUkp/bMsZZ3angfjinPMp4K9l56af8Gj1YIKktk2E/LpjXfS0J9bXCnwxGeSZsNBfwI3BKrytqObuhyxLxMsStg3/DDvxPO9ueIVH0ycQMYMs3/M9y/bOZW3VFnY01hO2nYdNDyMLn7+SlCSVs65/lLjE6DGyLL779CPWbN3Oi88/z4TRoxg75cQ217+vqKkEd0JWMmgRh6CopWQ94MS6uDqkow4J8cHDv4CmMP7p+WQmuAlpFlP6pPPw1D5HKyv/B+BfjkkxTZMvvviCQCDAyJFObMOoUaP47LPPOOmkk0hISODzzz8nHA4zfvz4NvuJRCJEIpEDvxuiiqD/lfgjrgfNQoxzrAqjTu/CqNOPTGYcnKyHvK5D2TKvkL2BdXSOHUBKdRpUAxjsXbSYgNePariJMfLY3biWpReeSbLXQHXFE9AzyErvT1FNN7rmlLGzxEcwlMH7d31KqFFG1xLo3D+Md9xwNr7kiLRItsRKoZAJbkh/8Qs2vvQVhiRhKAqGJJMAbNM+oPjbd8BWAAVsGQEVUPDt60oHjkONa4Wk1Do3TyU98cB/1et3svOdZxHWVVMRdyKRtARyLoin95SWdVJ+DyJBJ4daOUJdkf2QFS+eJJtQjYARcWJ8zj7rBbZtP5bdRZ+jqEvJ8a3l3oZGXqmqZF5WR+aG/ZB+HFOSWn+IWJYH0dW2dUJyJzL22G3Ytg6mjm1p2JYBlo5t6diWQe2mj4iUdSTt6tFOeq6mgR7B1jVsXUMwNOd/PeK4BXTN+RgRbEMHQ6O8bDV5kV9YEz8JCQPZDKIYQVS9Bp+1F9WKUEYy66zOLNheiQ2MqFlGx/rV1AsSom2RWe9HBHpNHkNOv5bVtA9Fv+M7sfOdEn55eg4nPHl2dH9Es3raeCBtK9nG6oV7GKELbJ/uWEok4JiNNl+PtDEzneVtaBZjUlBaxlcP/40YnHID8oGMImfqasVbZtb/htueyE+/9sACrBIBX6zTf7H5KkXlIjYCNiIrK8VoKrUTtI0gUslAEIdjWG2TlEbDuZ+62xGmawvpoosVQoSP5t7CVae+335jy6JWFCmNnmrflW1n9of9MG0wEXALNt1i4jmrw1CGZ09iaN4peNU4Xrz8RNK7dz5AUMDZr6eefxGDCwv45P33+HnpctavW8uFV11D/EElLEq27WbuzJ8oDJSSKMYwfvhYvPFeSu78AUHNQclp/pKwodARhDtLraLvCcewrz7M2vofmFH/DgUf3c5jU86gY8q/VjLgKP4Y/G6SsmHDBkaOHEk4HCYmJoavv/6aXr2cm8fnn3/OOeecQ3JyMrIs4/V6+frrr+nSpe0H5eOPP86DDz7Y5vz/Ftg4xcH+b0PQLETXvxfJrgWDVIRLSb5tAHbERPaoNO6rpu7b9ah1LoKiH4117GrYCEB9SMQIBhDEKnTdxiPW0/ekgcjr97FzlZ+g34NlWnQZaDDl2hN5+eONzdaXkDSFBY8NpEt1DKo/gmkYWJpGY6CO1Y0m+T11eglhLDSwNUfgy9ax0XB5nErKste5IVuGRf03i7ANg/D2WmwzASnVyQCxbZsZz64m5D4Tujnp14JgsG+uSLV6MmMnzCQ2tqlO0LJZz7Pow9mOB0Ns+iCYCKKFKCmIkhitLGwhKLmknf77Uh6Lt/5MqMY5L5JyEvj2rYsx5BCiHAY5TNidhK65WLlxNUNE6FC5htW53RlAPMf2SSFcV4EoKgiyhCgpYIvYyAhy+3JfgiQjIIPceryPbGeiicnIHQ8fH9AWln2zhfFLC9h09XH4fG2/1T8c/QA88eAmGiOJ3P/eBwCsu+8eKJqOxwof0TrTh3TF9+ZGqiqbtr8hKir3y4NP8rNVjS5ZZAwexm6/jY2AIYh8Mup8bvj1g2Z9uZIlcl1N5MbGRhBEDMti5Y6dLFi8iJhGx1qX20aZhUNRp/aEiEE47lhsW8e2DSKWgWUbYBuOFW1/kDbmgdpBNk4doZDlMJ+g1fa9pDGaTu35nSRlzbYlnNfzTmK2P8fnNeu46nAL2CZJlsX7aX3YkzOE1fuWkODNQBYEhmdPZED2FJTo+VVduo0dS2ZgWTqW4VQJbw3Z+Z245d77+e6TD1mzbQcvPPccE0ePwq3FsWztCiqteuLwcmL/iQw+eTSSIlF632sIam88PRtJvuSEZv2d+aFzrxnRKYVpIzsC0Pe9swDYYD7FydM/570T3mJwXtsijEfx/y9+N0np3r07a9eupb6+ni+//JJLLrmEX3/9lV69enHvvfdSV1fH3LlzSUlJ4ZtvvuHss89m4cKF9O3bt9X+7rrrLm65pSlavqGhgdzc3H99i/4fxf6YlP/bEHUL+QhIim3bGP5GkBQkj9rsDVELhxBEF75oACqAOymWlF4dm/XRvaYMS4/gSe/Iu7c9jL9yD6c9fcWB+ZkDezHxkpbrvnpaD57fWIu3xjFL52sduPXElsHVy8o3cMtmky97iIzJ7Nfqduz+9gssKRIlCuD/ZR2BleCc2qnY4XKkqBqtrWlElDh8Si0BPTG6H2SQg0iSfuCNez/Kdm7CNkR6Hd8TyzSxTEf63TTCRMI1yFLcgQJ2NcV+GvcJoB/5zc40NaY/+AIA/U/tjdubwdx19a03FgzG9FlMJHEXE6N/LWkpqurgeOi2MQXPg3c6MZeC4ARNCoLjjhAEEPZPBWwE/LqHsOXBFpzqxKvE6/k28yO873yDR3DjFTxM6DqJiaPaNsEfCt1wLEOqeuSk2babO0Yzp0yh+vPplH33LckDBh12eVESSYo1KPPHs+DDHyhYuwy/mYFgd0KNnUak/g0UU2TPPj+2zzkvZNtiSNUmRBnS+tdTsS6elKnDSX3yXQLvDDxQGNQWoHDrVp687Fzc4WAzPeIVq1bSu1OnFuOpjegU1Ie446ct7N7TQERPwsxyMe2q1454nxyMok0LoQJkqW0yvJ+keFvRfLEsC9OysCzTcczJMgIC3y39lPu3PIkuWc7uP4KblW068WDbK9bwWMVGLCBB2s6dQ25icG7zzKZvnruNuuL915dITHLb14koipx6wcUM2LGd6e98ytylywFQbZlju49m+OnHoByUwh3evIW1w/rhDggcenZ+cUYnzvqqgFfW1TANKA8016ES3EVc/slXfHrx+fTMbLLC6KZFUDOJb6fK+1H8MfjdJEVV1QOWkcGDB7NixQpeeOEFbr/9dl566SU2btxI796OAFj//v1ZuHAhL7/8Mq+91vpF6XK5cP0LZcT/02DTpqTI/1HIuo3aDkkJ19Sz+h/fs223QlBtMqOKlo5gm0i2QUArd96yDwNXUlMQoREJIypHdhwDpkVQFfACFVkubrykdQJbFwkAbhLUdvRawjqi3GT6NipqAYWkc9IQvR6kxN4H/M6R6nosSSUnz2brLgsBETVFo+uErZhCA78tupRwpUL9NgW3x03ZxkZ8aSLHX/L0Ybdp9axvmffu68QkpB+27X4smPGnA9/XzdyE4dsDed3olJhJt/wezFvzGxFbxyUoDMnui+n5niR9EpnZZ2BbBpZlOFMz+vZtmViWTkH4MUJpfup7nOO8iVvmgam9v7Kv1fSWjmUya5MLt+wiNykBwTb5Rf+VVTGbGSr0p8auZxPbWbBlGRNGnnDEfvyIYSECyu/QqLAtR9W1bO5GaudtRJjuWFmlz2aw7dtfESQJWxAxdQNJVRBECSV3DFJiXrSsjk3viEaf1CwC6/3sii0iM/AbhnEelrEPgMT4WPp1SKX3caeQ2a0XgiBQ/6Ej8+zqaxA5xsMecTPFb4zEFEIsjZRz1rdnYYo2kWAN7nACAEnnXIYeCFCx/DfGjhpDTUhjZkElX28oo7IuTHl5ADMUtejIAh3zE9hTVINa1r5g46EIBxrAtnHHxGNZJpnVBnNXVEO4jka/RiigoTUamAEDQgZiIMQloUdoWCrwD+aADQICwiFlOXYkr2Je5/eRLAFDsulkpDEiZTgf1X0LQOjBEciKfFBg80EuRNsGTBRgd62P3CyJE3JH89r2udy57DnuW/4clm1jIHBGTl/SAzq5g9I59rI7kWUPcUntK8M2lJRT9856zrTGs8uzDykvkdFnTUSUJSzbprImwPvLiklMcrPoiiv53m0CXp6Yt5jThg8kIZoROHRYT8Z8tpgt+Hj88Y8498KmJIBzGvwcFwjymKcb5725lJP6ZhLUTAoqG9myz49t2zx6Wl+mDc5BakUc8Cj+GPzbOimWZRGJRAhGhZIO1QWQJKnFG+r/IuZUNVCpG1TWhXApIi5ZQpFFxP+DpcZt20bWbdyetg/rru9WsqYsA0UMkGaX0qV/IkbEwNRNTN3E0Cx2rtuJrh6ZeX0/DD2MfBiSomkGH3y7g5oF+0iJ2FT0iuHBPw9rs32dHgbcJLnajvOwIwb2Qa4NK6BhWyKuXh2RDiG/wWggbW6neI758zBev3UupmHRsdMxbNm6BAGN2l0aDbshPssmLlskf1DzcvFtIeR3+vYlJBxRe4BxU1/E432S+MQ+mKbJj9NnAVBQW0ZBrVM6IENO4tp7/oxt28yf/TdifN3J6DOlzT5NM0TBr49h9DuZxF5PHXYMDcEG3vxlDv/Ubc7tY3L2NCfo8IEPr8etdeLtyz8E4LOf3uORfc+wp7SQvOyWFoPWoBsWv/c91LYsUpQ0zLm1xNpZ+ONSceV2QknPA8Mg0FBFWclWcAnYGHh8ThwUgoWkhrBtKEzN4cIxsUAscDM320/St+El1Fg//b3vkTpyzIF12baNIAhsd8UyFKhQ3bjCfgTbRLQt+noU5ihQUbcLBNiQ3JmGvPOI1WvonNqfgf3ieLY0i3++vIb98keCKuKJd9GxQzz9suNJ9ihc1j+H7Fg3x7/6DiXVR07a1i+ZSeUdd5NVYVB05gjsujSuNCYTxIndMyXALSF6RESvjJTgQsiQ2NWwhB7p+cS54xwxQpGoKKGAIEDtbBc5/gwuTjkDzdbwKl6umXQrbpeXabtOpOCDu7HMJMxYd9RXfRDJEcBGxLJMIjVdiLPy+PL0e3HJXjZXHcvG+nJkUaBCdx7s8zdt5dTqLITesSSl9TjsNhfPW03kh33ECPEIx8cx/thxHPf9WrbPX48hgBklDJJqYwYjxEs2o0MGizwKd+LlkXmr+SzVzeBhTrL4cd1S+K1Q5vV6uOutCRxqgPzg7FgeXe1idXEdPlWiS1osp/TP4pHvt3D79PWM7JxMbtLRrKD/v/C7SMpdd93FCSecQF5e3v/H3n/HaVGdcf/4+0y5+za2wi699w4WEMWKBbvG3hJjNDGm2JKYmERjjZpii1Gx944FURBQQHrvnV122V7vMuWc7x9z7y7LFhbzPP5+T8KH183Mzpw5c6adc52rfC7q6up49dVX+eqrr5g1axaDBg2iX79+/PjHP+bhhx8mMzOT999/n9mzZzNz5sxDV/5fjjLbG0iHr9zcYrshFaYEn/LWHWw0XHSlMFAYCnQUBmCiMISXddYQ4BMCU4imdV0IRjmK2IK9PNewBdNv4PMZ+AMmPr9JMOCnfm8lkMm1/zgZo51Ed2XXfEhDrH2itrbg2gkCkfQ29ymlOOPf33LGck+QDQHxCV34/dVtm3AaUWN7GpKMQPuaFGVJlNncVhl3wYmj+VqruuPV3vmDaUG2rS1COD7capj3ehVX3fkOAE/P/yVauIzrHnqp1fEdIV7vOemGUtM7fYyu+znqtN83/f31zA3UJ2erffzd2BUvZuygUd51WQ7SiGMYHUcPSZlMlHcI1uFGfL56Af/4OkC6v5bRPZo1Y65y0UTzYJqfWQAlcMYXZzMpMZasSBa3Tr+L1ANI+Q6G46rDngUppSh3K2hIayBcE8adegs97zoDI+wJnHtXrWbA683O9aV2CZbpoyolwag7z6fnvDWt60TgT/X8Umo/2YP96esIv8CpTEO5cTKvHMzODXGu7d2DL6e+Q5/uzX44dyV/rlKMfGEk9XZPVpeCjKexoGgzMwAzxeTEo7uTkxJgeHaECwfmYraRaRmgpGI/jtV55+qiJ/5Br1Kv7+jxzmJ8ORPZNBim3TWWrplhgu3S6x/dYb3PL3wLnxXk+ukt/QGLt+3l8/te5ezseyhXLrWxIBl1FpZSpFw6mOyRzXxCFVUVxB7YQN5Oh1XrtjFuxDAeP+tLACobCvlgw5Psqt1F6PNqAPJGTO6wTdJ1Wfvsn7H9exFDTFZmD2CdexR1n65lbQimRjWmRMKYAvyaxglD89hZWEOv/FTyu4SprqmleOduTiDM14VFjE3Of6768dmctb+CMY8u5mPfdZxhPQun3As5gyCtO6nZA3kgKXc7ruTuj9bzyrd7ADhvdD756Z3Le3QE/3dwWH1IaWkpV155JcXFxaSlpTFixAhmzZrFySefDMAnn3zCHXfcwVlnnUV9fT39+vXjhRde4PTTO2/H/m/FouoQS8tq8Y3LxXIltlRYrsSSCksqYtEE0T31FGfGcDP82BJsBbZS2Ep4SwSOgnrAUd66g8AFHCHQrASjACNuULlEYEgNUzbOZe3kLxNfogo90L7Ww7YTmG1Qh3cE10lg+Nv+mBXgt1tGm1TWx1m7v4yQaRLymUR8PoKmgU/TmswJNVYCU1kE23HuBPBv8XggFtz3JlJX5FUGCDhWmyaJRG0y2VpakI9eWwd49QrR3DY3EUf3Hb750Yp6nCD+yHdPyNe9IUam1g+y/Jz1wwsw/KanbgeceB0IhWF2XL/bmBhQ65wOI2prgOSysXGmjz++uR7lomHgOhaaZjBp3In8tPg63iubydf+5WDD++/M4uUJMxg5eGybde+LJmgAZsxawai+OQzp1Q1TF+2ai+IJi73lteRKycA7T2PzO3PJWJrNzg8W0f9Sr23dR43k0XcfoKDfYroGLXqs/CWWFORn52FbbQvWJfG+EPgWzQmgx4O4rg8adIQZRphhKl/fh5XumR/87URnCRRCKEZ2S+OeKaMAxaL99byxrozfHdudbhEfQkFE+amvjKPrAk3X0HSB0LWmv1Oq/KQ5On+563L0gB9rTxn+rpl0HTCIipr9dM3vw9mnXEttfTl7F85G1NThaDBg+VIMzSD21GzYCL1yw2iH+Y0eCOmCpin2btyFEF6qAMNn8NpdvyQ1mbVaFzoZdRYNQFgI6nfXthBSwiJMDO/7Xv50BYuMzxh1UQ5jRg+iS0oB14y/F4DCQdt445e3sPDJF4ik5jJ65JRW7amv2c/Xsy/B7Lcbf20PMBX/Dp5CpRIMiLucEBXcOriA0X1astV2O4D3JD0tlfRRw0n9+BuqY3Ecx8FI3qM1q7YBoPU+BS55pNX5Y5bN658u4vGlVZQ7Bj0zQ7z546OZ0Lt12oUj+H5xWG/5s88+2+H+/v3788477/xHDfpvRZoDxzsGOWPadgou31pM/KtK7KG59D62NXlaZ7CnfA9nVJ3Bbf1v46ZjrsCVLnEnTn0sSkM8SjQW59sn72fIl+sRon0WYMuxCYcOb7CVTgLT17ZmRhOC9248mljC4c0nF1C/SeHfG+e0TcWtygql8EnwSbCMbCLUseCTYxHKRCmDZ+tradAFhmZgahrXBn9Ev1hvEmEXzRVY1XsxirZR/ruP0IIhRDCMCIbRgmHqtgKMpsFxocETUDKHSs6+rrnTdBJxjE5mjD0QiZgnHBjmd8vJAhAKZjPE6kXvW05qtc+J1Sbr71iTYtse30dRQzVa1W4MTU/eKx2fbmJqBoamY2omhmZwzIDh8MlqHl+YQ1bKB1xzwtkASOVgiz2MesUTQO7tehI/PutRfswt1NZV8/b8V3m0/EkqqvbRUJFNOLM1d8lnxZ4J7O65xYi5+1CspqvfYs6dp1NWG6U+GmNIL48JVCnF+X/9mBxX4ReesDHw/BPYvWQuwTUG7gUOus+goqICPWsLXQrWkAC0bqAXahTt38fHf13IDVYFQwdOID64O7dWe/WsrTiGMduyOUZ0JeHPYeA9xza10a1tYP/DH2GLJLdISgZtwZGeNmNVUQOnPbm8xb7Lt7TNSdMmzAGMVrXYlbU4MQeREsDZvp+SjaW4uqLQ3cLjr3zGKWu3E5KQ7Yequ67DnySLc12FkM5/JKA01hOvq+LNu3/aal/eMdPJumwc5Q8uA2jK6+1LaSn4Stuzb/U9OpMqbTt8lcaGV+vY8OpSbnh8CnoykWNBfj/qszQi5ZKP/vUIox9vKaTs3jSHDZt+jp4SJyd0C8Oner5au+auAgEzzxh1WNcWScR5sltfFr/1GZ9eciYA2VmpQDn3LK2i94CNDBrr9bELlu/jkZnLWRnzTFn5sf3cO64Ll11xRnvVH8H3jCO5e74vuBI68D9J1HuDnD/lu6sW6+KeSjvsT86ENJ2wL0zYF4akVn6nboHWmo30QITKKkmL7mPpRReg/AFKUsbh9u6N4TMw/DqmX8f0m5gBA9PQcexyXLuBsr2V7F67HV/Qjy8YwB8KEIgEmrQBezcXUr/J01qkRRP8LTvgaZEcl5jjsHrFOiJmCgP6D8GSihq3gnQ+IMM4DiUtyq0alie+ZUgojbDhw1EOzwx/mjP7ns0lI28DYNnk0RhlcSp2K88v1IXGUIWqnLEwZDTvv/ktAXoBUL4eggdkiHWtBKEuLWdrtmNxz68uRKu3Pe/nZKRMUxSEEPjrFBEzwobffoDSFcrLQ4jSQZMOYv824u4uNL8fLeBH+APofj9aIIAWDKIHg2TWKCqDZahlm9F1LTkD1xG6RkXp18RiKRTVLkcvG4imGeiaga770PBhmn50w6S4znPKvK9qJGurOx48hZJoQqJP6YI2r5L1tc0ajgqWt4jw+G3xF4zYNY9evaaQmpLOlCFTeXT+k+zc/ydUvJJ+6b+n55iWoVwf/PRYPvxyK1s2zqVEBqgNF7Cxxse0+z5kVyKEicvqu08jFPBRH0uwvtbHYBElfMBYWJdbT3ppejI/EmRmZpKbmw6AkgMp+OmJPP77Ryhza0lTYS464WyGHO8RR+7+ZilLKuoZXFnMDlHJCDGC9HEtHZv11DDd/vQD9M/+hVaiYbZhJoRmQrjjuwpOOqY7MklVL720QTgxm3mLvyFdhZky6ThcqVCu8pZSIZPLihV7GBgOcNojHzbV7UqXWquWjEAGb587jd0+nf2TR9P16svoM2gMGRnNZjjpuAj1n/v4xeujgAvCz/FX/gLXdnBdh9SsLgw9bgw1uzwBs7ZbGH/PVIyQScHkgpb3JKkdTUmLcNkpp/NExdv41nZNXpPEVTZmMou2UecAGmZNc04ppRTLvryPKud5hBZi5JBXyO3e2ketOFqNqfvI6iRL9wuDCnh0yVq+ys2norCIzIJ8ho4dzNtl5dzyWRVXv7SCP9QqluyM8/yWYpIe17x6fg+KZi1iz8wPiZ17KsFIa61a3IkzY/0M3tryFgWRAn571G8ZkPHdw/SP4NA4IqR8T1BSIfT2PcTthoTndxL+7pFOjUJKSgeOpm5DFMff8WPvW1oNKOTuPdgij72jRsA2hcIGjGQmYAlYuNYm7IZPAHBiq3n7np+3UaOOETqeSM/BgDcIGK5JYuZerrzj5KZSf//sS7pn6px7XGPa+wFAs1Zh85Z1sOgSfjnofiYOb53rpXjfcq75oUMv1+Cja9eClCg7CtEaZH0V+19cBHtB9O0P65K+G2h0n70UTSl0FDfV1VBTV8PvrroKXdNA6IAitcGlLD9AXrduSCWTTpfSG3yUiyi1Gekcg/QpcBXCEQhLQ1OCFLJQ4RycT19DSIkmJZpsDkmXyd+ePn1YNmE8zNzc6to8nOOV/OLlFls110dm2VEHbHmGaT7JD66JkZUhcaTEVm6SAdXFVgpHetvn7zZYssDz/VHBXk01nFkfZK0/xiX5k5lbupRviXPR3JuY/4MFBIIZWI53/7526sl0YUf5Q6RuG05Gv+Yw4e45EW66ZDTP3L+QsZpJvzNGcOlL69mV8AYbG51hd88i12fx4AWjyDZtLCXQlINybIRuYGYGoRQ0s/mdPf/8ByguvoXcXE/gqBMxhmT25fwbL22avQP87tjxAKz5LMy7i/fQ+/rhpHVtO1GlJW18yoR2vtFGIWVkfjo/mNLal0paLrXfziXFb3Luae0PWutXbkMelPNK13QyAp4GZ/qM1/jH9ZdTGLU56ejWs3klVbsJSg8Hpj8LJauZdOEtjJnW+lty454WSpTGUF0j9Dy7dTSOdLx2rN9ZReHaANdffSFX/G4Ogy2D9Z//gtFLX2J9OJ3XYuczeVc92VVVrCvI4rPbLyQtP4fajM0Y+TvRrIFMOeVV/IH0FvW/PsDhB1sMRn+7C4Ane8c4t1fHvjYAw4cM5FfAJ/tjDN1axhJX0qNnd8adNpkX8rty2b+X85NZOwHQlcuZJZ/QVSqOHvcUdb1yeean1zLnuac44+ZbW9RbE63humeuY0fKDqYNmMba8rVc9NFFXDnkSm4YeQMh84hz7f8NHBFSvico51BCioUB+COHRwZ2IBqFlEhHjqaxKE67znYego5L+Nhj6fHvZ1j5zGxYDj+8dzT+rC4ev4rlkGhIEG+IMWfGZvauMzj5x7eACOPaNnY8gZ1IYMW85do5b+KqMuIlXqSM1Cw06SN0EJW9o1x8Rvu+FHVxz+SREmwthDl2nFNmXw3AxbnJjkzTEP4I+CNUIVi/1/Nf8a9rns0VnuznPNmA7g9gK4U0fdQGM9hNDkPyIijpUtMQY7eWycSzTuQnJ5zaZttmvvoKeetz6H/3ya327X/kfayiACPXNDt1KqWQiQROQwN2Qz12tIHNz72A37a58tIfIV3pcVo4LkpKpCOJujuRERepHI+3RbqsWVpJUVkF48+rR0pFzHb4aqtLzpYsnJJMzh7d8Szvibdm0eiS88OxzVE7V9T6afAVMP6HT3AZ8OR7l/BE7TrGv3kcF/sL+Onpr1CgslgWLWdZ1ODMNAv/pjsYmfpvti5/gIS7n5DZhz4jb6RrRg4rizdibW+mMg8LG0NT1Lg+iq0AV7y6CTC5IjiH8ZlFcI9HjtZfaeA34Y8GSsQokjn8yL6bKtLRxDo04CSfzYaK7Sz962O0CJNNIjXmPe/Sv61ih27iInAFyAN+tZFifF2Mdv1lGs09B9PiN0Lz6aRGUnEOyh7cCkpAB+GsvrR0+tfGEQ0V7PvdXQi/H83n85bBIJWbY7j+4az5eg5WPEEilsCKJ7DiDlbMJREV2DEDx9I90kHhIDSFpgtslaBGQlwLEZJ5QIiv1hcxf9PraJpA6AJfSOeKy04nkh+hOGhgRhOsWbmYLdXrkFI2/5TETjgIw+LtwjyWvFlCBhpVfokT2Mq9Sz3H88E11Vz29qdN19dTq2FvpQ/z1BUYQKo8k3FnPdbmfT8+fxyPJxZQbTXw2+I8yuOdYyOPxmKY8Rh9qivYkZ7JzfOW8f6V3anfXcxnj+/hQpFFLFDDpTedSI9uKfzjqjeQTg2bFq5m8LGjOPWGn/Ppv5+k6C9/4qgzziYnN5fKyko+nPkhw6LD6NutL/dOuhfbtZmxfgZPrX6KuXvn8u70dzG/A8vvEXSMI0LK9wWpOjT3ODFvNhtI+W7SeHXFHjZs9fyBOtKkEI0jO3CalVKCUujJnCF21EJIA19qcyZb029i+k0iXSIIIdH0ACOmHt9ufTvWPUUk3+Cin09G0wQVJbVsWb2bY6e1DO+1ldOuuh2gPtEohLX2y6irLQTgVD2dy8/4V6v9aX8fxkWZ3VnVcDZb4l5b004v4KbpLQfxX7t/4e0vtgNw4dXjmd43h8+3rOP653Zzembb/goAIqaIGW1TlStberTzB5YXAj0QQA8E8Gd6s3s3GCSQiJM/tFc7ZxnaasuejTPZV1rFhFOmN207XikeuHUe1rpKmNbqkBZ458ZjOP2R+QBsKq9ncGaE9UU1LGjoQX9f8/t65UmPkPXNffxp/1zeSBQyb+6dBCMOJHMIFtkasdTtLF53ArqWQlD2o4JZ7F/3PqXOVFzyCEdj+ISLpXQalIlwFecN8PPL6eNZumk3UhhkzwlTFo2QGH6F591p24TifkKREIHtj1CglXB0txx0I4KrvPcrXqYTD6eR3jNpjhAHLIRgb3Edo/bD2rwIfl1DSAVSIaTnAyWkYodpY3fQHTpuUkjR2i+TkZrOjpLdHd5vTQlkOxHIxV98TuHDD9N/VxEANW+3zqtUMuVuABa8DOAH/GhGHM200X0WZiCBGYzhT3FBGaB0pBQoCU4ihuaCrZonQvFyCVIgFGiOjhbL4NtRKzlhzDEM/cPRfHLPJ6xxdhAuCqALDU1oaAg0IWiQCWJGgoCVQg8znVLbAQ0yRTMhoXRb9nlDjhvCcbe9xJL5k6l3ixHWbKzyNfizW4f6u3aCSTt3EDr2An5bvJWX9jvMq/qQAqG4b+L0FoLN+/MW8s+SGnrU1bAmI4vCLlmQnklBdSW3D+oFQCg/G9hIihPjVw+c13TslQ8+xou3/ZxP/v47UjL/SUavdAZcvIf6+nree99BKe8a0jPSiUfjjM71zImmbvKjET/CVS6Pr3qcBruBdD297Yd7BN8ZR4SU7wnKlS1U1gdj59YdjKAbe/78DVIopFAooVACpOYtK1yNWTXryNr/BYGCrqR0reT5AeezszSXUfVLqfbVQxieXvBXCvxdCRh+fIafgBHAbwYImEEGL/NyWcTnv48IRjzH0lAKWjCCCKXgWsk8J2meE4sds9EliHaEh0Q8hqa3LfRIKZn13nH0P7uYWFl/fH5vlpHbvQu53Vt7zdvKxdeBkFKX8MJ8U4KthZT6Bo+o64KhV7d57Ib8qYwsnM3J6X8j6/zJjB7eUuNhu5IffryWeQsLm68t6RhY0eCNxJnB9nN8aAmIm+0JKQrkIWbYgCVdfIfICHwwyvZUIZTGnN+8hhCgaRBM8REeM4CU+WU88sY6fnlx25l/AYbkpPD7S0byp9dW8+7mUjKFzlXPL0GpS5kW20ZjTttwSlcmj/s1c594iV/n9WZpzSqESnB9v+PJCmaTXTwPIQsxEhmMO+odQpn5WNEaFiweQ9e+C4hYt3PO9GM5+1yd0qo6iitqyctMpXee9x4UTPJMKIULfSSCfSm49P4W7fz362/xQ2BJv1v4/eXNmXaVUvT6TSXTj+rF3dNaC3EAf56/lQc/2cJX146gV2rbPl8z3v6UeE37s2A7ad7qaKYcCAWoJcqnz7/HtGvObbOMUBq0kT28fMkSqn/6c0y/j/i0Uxh23wPoSiFjMWRDg7eMxWj45l6K/XGOnf4EwVAEfyAFTW9H6jkIL355FBlCcNPURW3u37ZnD7P+so1EtDlKyjcsA1bBj39xI6lpLb+7lWt38cE7M5g0Jp8XfzAJpRTbKkoo+dtfmsoYfsm3F0extnZn6Ij9RHuEWfnMH+ji60VOYCfVaS5Llp/DsOwbyBh7e9Nxsb1b2f7Ti9E3x6g84y1+2rUHfVbsQ0ZNxm5ax8qnMxgz5bim8u/t2c+6gt4klCTPjvNLt5bK2nqunXYCoaQjfM0Oz1/L7za0uI6MrplIx9PSbF72Jk7Gc0QiNpFIFaGQzvhxzxAMBrGExdN/fxrtIFK8M3qfwVOrn2LyG5MZmzuWiXkTGZE9gmFZw0jztx+ifwSdwxEh5fuCq9q1dwMMOmMMa95fRkFePspVKEd6x7jerA9H0a/CTzxRzXogXlhMokijujYTrDirGI4vswx/GL6yl6HHBa7eesB7M7ncef2dHTbXCHjHOgkbrYNx0442oJttd/wrF9yLLyMZwRPZzapVr2GaQQwjhOkLYRpBTDOEzxfGNEM42JgdmHvqLa8jiYRaa4qiDV4W43CwbZ+D/pfPgPvzAaiq3Ntq/1EPzKGi1kvMdtG0/jw4pVnDUhX1iO2ywu0LKUZCYPnaDoFVbud8CBJSHbaQEm+Ig6mxc78fhUbCTIUyuPhEPy8s0wjNLaXotBj5ae07ZF84MJc/AQsW7mXBwr0YmkAql5zUAOU1pYQDERqi1Wye+3emSMkVvS5mtj4ZF52/WH7+llfDyWN+36peX8jroB27Fxf94kpvG9Arrwu98toO7RTSRbVBCli29kswYdDUy1tsl8n71REJ7sL1nmC+qybWrpCScBLoHVDPWUmq+fbMPQBTLziNhmfr+Xb3asZsnUBu//xWZTSlteoHpJTs+eUv8Avo+fprZAxuTqSoBYPQpfle+TYoUn2b6ZLd0om1MzBVHEdrP6RWT/LiSNX8HrvSWzfN1vdGD3jl83t4z1kIQf+srpQH8sFeRyyWgxp2Eb2Db+FMKSYO7C7ZwY7SLhx9zCIgQGZFAtfQWVn1NP0XbKHg2GeQ0mH7Zeeilybb8fF2zsfTbroB797tnTO/hZDiCpi4bw8fXNasUTwYbzy4GowA/oOExMp9XkRcSvd63OxnIJ7OuNFvs2zlVFJTt9ClS5hQKIPiJMGiOsik2D21O0+f/DSvbXoNqST/XvtvLGnRI6UHL0x7gaxg5/I6HUHbOCKkfE9QrkJ0YO6pd6ohzabI2oPQNbSAjmbo6IaObhjohkFWBUzM3kDpngbK0nLRzJ4cWyWYmRw7rYqp2OXHs/rWo0jNyvZCkK0YsXgDsUQDMStK1eZzKLBT6HL671DxKCoWRcai3no8hr1zE2Uz1xPs73np764IY5kBomW1mKkhDFNvypMDkIjVYrbjA1Ne9zpG0noVDG6lovJ3Hd6jyVMgLl9jzuc+NGni+upI2XQxvpKjcTVJaWgXwawAhr91h1kb9QaiSLhtWnrXbfZDyd32MUy5tsX+QNCApJCS6Wv5WVTGvJlXVrh9M5qR0HACbQsjylGNYUYdwlUS4zBzJ3RP2c++6mque/YOALa9+zWzPrfQfCbDTuvJlvd38uK9S+g7rQcXndC76bgKy+HclVvZGbOgzmrKQ6MJ+OKXU7js0ffw25VkPeo5Ma/pfS5Tdr4HwHHH3cR2w+S5rfP5U0k2y6orubid9llWF0xfXjt7W0MoBylaawYuvfpGeOU19m5ZzdBu/Zu2l8UdhILdlt3qmEb8dGpfbnp+OR9vLeX4NjR4AFG3tgVfzsHojCbFF/Iz6bTjWfvKVnZv2tG2kIIOByV/XPu73xAsryR+4bktBJS2IJWFcNvvRzqCH5u41r45WRNeve4BjrkyKaQYbWiBN5ZUA3D3Rxt4dlUtPTJD/PWCkcxMTOPXFdOY+/B0Fn15HE7IJVyaSUNGFbl5O8jN29FUR0Wmn+FpV5NatZgt9ldUvDcB4yWBXuqS+a8/kHPcD3Dr65F2AhHyk7AFW48+Gn31KhzXxUhqkRwERhv+SI2o3rQL1wjQ3VfMyXe1dEjOKshh4CnDCfR8E7u6Jyee9T66EcJxfBiGRSCQFMIaczi1cZ6JXScysetEAOqtenbW7OTnc3/OpR9fyj3H3sOEru0zax9BxzgipHxPUFK1GNwPRumiHQwszKcyVIcmRfKnoSsNQ+r4lImLyztDzubYt+9h3qRf4xpBso/ZQd89gu1V3kBgSptAklBM13TCgQjhA4SIct0hkZlL8IS2eVJic9+mbOZd6MkEYHWWN6t9/q5lTWU06aDh4FMJotF60tKbfTWklJQVrcIw/OCGgTjUjSG/1/lkF4zGtqNYVhTbiWJbMRwnhuPGcKwoiT0VZKWmogUl0rUolP9ie+YSssOjwVG8J+YQ0+Oc+9G5mJpJQ1yQQm8iZhd8NXMBKHv/BdKD7+MPpyD8YQhEEMEI727/ksYA2ZyS5dRYDgFDw5ekCn/6qnGc+eA8AAxfy0Hgi93LgLGc8v5paEYIIXSEMNCE0bR8v/oXAMz5+1KkLpC6QOuVykmn9QcXEIfWprhKYGgB7LIoZnYI6bps37QBJOzcuomG+npC/iDjxo1P0ptr1Mdi+EVze10r6TsRMJl2SgFZvVOZ8+Rayt7YyScBg2GjcrhrWxGzyj2tVO+gj8qqBInG44E/fLONBunz1OSeGxATkwLKqm4nMCoQwQRuHHwy/yyZy0u1vbmpZi+90lpzAAkcNNF57hhNuTht+X0kU2scPEBYye0Zvva7slFdvUHG30EOoSLna+jAatKoSTmUY2SXgiwiBJm/YiHdeuRTMLJlCgENDWG07AcSe/bgA1LyD60d8YSUztPqz1z1G6orvgQE2SJOpWhfSBFJ82bV84W8/crL+Ewo7u+9Ge+9+ikXXnlWCz+QQHLSJRGs2FPNij3VvL9yH2mJEDlOFQsWTfDopYEJFy1E0wzmzf8tjvN6Ux0Z8TDr5PNMmvAlqVtnUHmjp+vVzxhE9mRP9NUjEXS8Pszwg/XQI/S95Wd8deJ1bB50JbYOR5GKJiVPffUpoNCURCgXgURTElczwYjQ+4QhBLPTW1z3trXzCfR8G1V3Iief80+MZGJG287xeGkOnjgcQtkZ8UUYnj2cV894ldvm38Z1n1/H5PzJ3DL2liPhyt8BR4SU7wuu7NDcoycEhWnlTLmj7Tnpin3buGr2udzY9wH2nnAWk766Df+/nqdg4mU8//s5TeV8ysb0tx8h5FMJEm04njZC1lR67cnwVJTX/nksu2evwG5IYEcTODELJ27hxG3qohq1tfUE483XtXTuH6gXrwJgNCoeUlawa0s1g0bObve8AIxv+eea2TOgWwonTfZSqz/09rPQUE1hXaEXXSBtlLsTgU5ANNDPsRhf8VqbCVwbBZSfD7yDFSmD2frNuqZ9hqswZHPP84+31vPyOxsJCIFfCGrlOEzhML7XJVjSxpY2jnSwpY0rHRzpsCNSSbd4BgkNNEfSpcxClifgtP5JJcqhhRRpZqOrAKse+YKP/SvwSQerjQF70cZNzX+YJn0OMBG5tgsIdL/X0Y7vn8mw+yZx/13f8O99+9kfr2hqycmZqbw0og9SSm4ObmRVYTWF26qZt6iQEaIEt3w9ADvSBtKnxguLTqlraSo7OljJx7EMjlpRwbVpy7m05zCGZfZrLiBsNK3zEWumEyPhCtxoFGGaCF1HaBoV21fQE5r4UhqRkqSf7xZoX3gwkvnE1uypbr8MYVAd8BjZ3mCtiY61GGbQz8UXXMhb77zNC++9wq/734r/AB4eXRgIo2Udo59+hhVnn0XqY/9gfVERQ/98b7v1S2Ghted52wZqKr6gq6igmBzKVA4DC9qenEjHYc/cV8jdr3CMADF/GvsjPRAlG8AHG3auYOea/vQZ2Uw0mZaMEvz7JaPJzc3jpEc9B2xXCE7b/S39Ij9jW/0/ANCS7/HkSX/mqzlvozRPmM5IO56qxMdsWHIlo05agLjVR8VDL8OXG4nNfI7QWddxMCacdhLf/u2f1L7hRcuFTuqGW1tH/v5ishzbowZwQSYzlEtXUdpgUpkAf35LTWu0vobtu28H1ZcTz/xHk4ACkJ11EfUNj7Bk6VNMnPCTJmGlLU1KW8gL5/HCaS/w+e7P+duKv3HBhxdwyaBLuG38beha55/h/zqOCCnfE5TbsSbFSAjivvZV1mXRagCygl045ckHAS95nOM69EkvZUd1DoNSopRVOR1mqdVxvLDcdiBrvfNoaR79dTA7nUGXTm23/OMXv01acnZpW/FmAaVhKuldhpGwSnGdKD3GtM9wW7llOWW753raBk2BUAihkQrUq2Y/kFkXzGpx3LDnxzA19wr+fsbN3r2wbDaNHE7xFTcw5ZrpyGgtqqEGFa0jWruf5xfUENo6kmtO7kfcdonakrjtEndd5J5aynpmEDQ0+iuDuOOScCQxR9JQ2kA34eO2Kb9s9xpIapAbh+ePX1hFxr5Y8qYqDjG2AVCiVZMiA01JJxsFlLNOnIqSLl0zs3j5r39hePc+FIwa64UxS0nPceOa6vA0KSb6AYN20G/QMDmL4ohNjmlwz4B8puc0a7/2Fa7lkthbXJzhUD0+zPO7sni37i6+9o/EjhtUDjqPjOWPk+HUUhNopkUH+PfE87hv7Sf8vSKf52p68dyaevZNkU2JRjXhoneQ1uBgLC6/ml1FE+CXi5u2CVw0urFUvErDGwE+f/NLlPAmtAr4sfLDe/u4f2YxCoESklWDJVv6JLlolMLNDbJmbSlD7noXn+5iai6GJjF1F1OT2CndMAJFXPX8D9CS/4QQ6F4sCw1uFAyo/mQHq2fPRgkFHEDoxwEyjoDxqh9RO87uhxaRmp7m+cxoAj2peTsQZjhMjz/+ierrfkS8uDUL84GQ2IhDCCnFNZtZuPkxNM0kT1RQTC6XT13Y4THfnDmZrF3V1PZPofuvf0duYBAzn1hHtCobcj2fjT3fzG0hpDiN/iqGTr/cFLbeM43S+gQr/nAf6WVb6DnhebbN8YSUL77si1ZnkGYPQGU2m7vC6RcQ2PU5dYk4djyK+MFPqc4QdHv03+y+/SH2FCY49Sc3tmrvxFNP5KuF5VQU1vKjc9vvnxqxbn4R817djHmQqXjhl3egBWsYOez5VglSx4y5ljlzP6C+/mG+/dal96CLDnmegyGE4NRepzK1+1Te2PwGD1jInj8AAQAASURBVC59kB6pPbhs8GWHXdf/Ko4IKd8TonU7qNlcSmxpX3QzgO4PofuC6P7kz7JoSK1HStkqkzRAeYPHHpoVahkGa+gGc+64BoAr7n+LOq39Gbtj2xjCReuMkJKe06nriqPYVFPO/svPp8fVq5sGY00LMPLYtojdWmP7loepDSxDk35wNQQChcSHTnpFvzaPsRwXodmED3Dara+pAyUQXXLQu/Zpob33A11ef5NhOWWccvSJrepb97tv0IMGg387sdW+T19fR+7mmlbbO4Qt0XGR9fVIy6YzmhSAOi3O2cNHsGW9xymiuQ5jJ3sOghWrV2FE67CKCxnxp7Zn267lAib6QZ1xJGRiOhZrTmgZ6ePs/paC50/hQEPDWcmlr1s/zEvnME43SJx0C3tLdzIwo6WfhRCC34w4g+saShm5xGtz1EkQ8SXzImkOut55TcqexDAywoWMnZiKdD3SPOlKbMdhV63O/mA3lFQo6UX2KKUosxxSugTQNU9y0VbvZ0RFDd1HaB4jLIraCTrle2GUXoqSCssR2K73s6QgkMjEJ+pJM9I8sj4kUikkEheJqZmMrhtCH3qALZuEH2+Z9FZQSZlFQRcVIUulELJMnLJos0QF6G0IGUV33kkYGPznezq8PwoXoToWUhZufpT0+tnYjXKUcWjHzaxd1QBM+/DbpknOtc96pgkpJX/605/QDpr9207SXyXpF2IaGvnpQVZoOhkN1bxw6b+xuvwFgi5WnWeO7nbUM6RmgmYHkGactfuvgSAUVeQw//57uVl7juEqxj0X96b31zDo1a95fPUgBmeXouueidPjWNTYVhJB/gdJ5Od/9ltqSmNo6nw21G5mg9qEEh5ZXnxbJbk9q+iVeyJ73H1UVT/BrqSVKrakjH/ufoyYabfQquyrD7FfpGEENXRNJEmpvXupCYEQBrLqBN5cs+CIkHIYOCKkfE8oGvlPrJTCJht/KxztJZb/cu6DzYkDlYaDhkTDdRU/y3FYv+rnrFsaYs7m6WiY+DSBqYNPE2ys1kjXYM3iRWxevw7DNDFMHdP0ed75UnKi7pJS/BVlq2ah+YJo/hC6P+z9fEESez11vkjpXOjcwGGj2bRuJXv6DaWntgqA7hkP0XfKmR0eF6supXLXYnTdhyXKyLCOY8zpLXNDbfzNZ1SMbtvhtDrmUftHDkhq2FDl+Vn4UtoWwmzhwxdoO0xYlwpltNPjWS4J8/AcWntUriKjpj/77lmJFuyJXbiULaMHIZKkWd5SgyT9vTB0skadRl3Iz+dbvm2qRx4QTWLEvCij7bUVfPKzG9B0z7F6+IWXkjfRY5x1kyygK56dh5kaImtQAb0m9SfqupiytZraTe2KgedXoKFYkT6SITUbCSiLuuzhkDy/3wzQPb/9nFLOASanRoWhlA6aJjH0zmtSotUvEa1qoN8D76MflJ9mdCfrmHHX63QPxDlv3NWdPu+imR/g13oy5vSnO33M4cK1bYrvWoxKaeNdioShtJy9773LwBtb59NphFIuoiPnGUC6DdS6OueevAXHtVtks+4IZccOYnAbWlhped/MnJJiNtx+B5rwBt0oAvw+zIMcjsf85CqWmAHqa/uQqsrJlTaWuYfddg/2Lf4RJSsuJTtgMuqUKAl3C19t20h1dR42Jq9rZ7H26N7MLXwVTlNcu+wEfA5sLMtBd+NoykWhoYTA1QNk0rHmqRGNV9XY0sp9DWyaPRK74USEblGiuS0KSKcvdaEPyeoyBwMd6epU1uwhpNIZW9mPTTX7WBnYdgAnj2BRfXf2q1R0owGlDlCxNVarBEqeAuHOkdIdgYcjQsr3BD1HJz/jUgq6XoMbjyKtGE4ihmvFkFaMTWVrKQoUkRZOwZVxXDeGKxMomUDKBFYiRlmVRQ+/oFuX1Xy97TjKY9k4imSWZA0XwfiCMJ999ilR1fj1NH8oGpITgXB0N+H321Zd7v0yBzBYM2Ioji5wTIFtClxDx/FpuKaO9BlIn8Hu/EH4IzmIggGARmHhYAoKNrK58B6Kaj5G18LoegjdiGAYEUwjBcOM4DNTKd/6CtFAkq8hCKn2mBbtiDkxdDTq3Oo227mzwtseTzR3wNFqTwIMpLUtpDiaH187ETjpsgyqXWA8zr596NnZCNNE1ZSRXl2Iox8e30Ei9TNcNYfswRdRt2InmGuJnDwWrATKsVGOg7JtcJzk3y5jZAH7pEQGI6T7UyirLMSuK2uqM+2oozj3sh/x5UvPsLd4LxKoDwQomfE84zaWkpeSQaS6jhw0dm01cIWDvXwl10zqT9RV+NswpfszesDdNZTuXkHe8ydQEC3C/tUWAg/3Qm8jFLg95EdyITlguNIGgiSSvDbG4dCFKy+SqrPcH23XQYcmz7YgRQL9MHxnvgsaCRsP5EvaM/ND9j/8MFrAE+Tk3x9HXvcjNH/b917hotPxc5EygZ3k8jA6yYC6P10gc9uOfDICAcaHQtTU1uEaejIVhMQXj9OjuJg0q6Xgnz+gF5PvuJk3frMI/4kjOeW8gQCsnjebr1/T6ZqmUVTm4+sP/HQffhJlpXEQsC+1C3/4xR+4+pPf0b1M8c4tKwhe7eODL+ew8x2LuvQ6rvzV8XTLbjuCr0McIKVIV/L+oyuwG7I4/45h5PbMbvW+PHvTXPK4ismnPka0fB+VD29vQaU4sP8ALr7qhy2OmfzH9xgXcnn71tZ96+YV8xn4oaejXJZxF+xeBBVbIVEP46/zPIKPoE0cEVK+NygMI4VIWp+mZH8HohvndKqW9Uv+TUn9fTzxw5PJ7No2edV9v19BbiTMT267E8exScRiJOJxrHicfbELCBDFTXiCkrRjKCuKtONIK0Zl3nbmLfyCrILBdA1noxKJ5M+C5E9YNrbjsj+jgLRYA35Ai9Vzzlkv8PFnP8Dvb6Cubh2aZqFrNrpho+sHkZkFIF7dkynH/BvXiRHJ6dtid3W8GhD0XdOFuZte4bbejyF1iY6Opmm4jvdRv/ptCftmP0PIMEgp2skVgO+BP7Ft1WIejGRxvCbJSk9D9wWReipVVttOb10DVwNg3fc0ZnwVtm8oKpCHr3YWRwvJPt+JQOscJ+3BdKspz0yj5/TphNqnbmgB7XdvMSQnRvrPPE6Rz//8KLsKt7Uo02f62fSZfnbT33/6zR1s9wXYvmsF18ZPIEwBR6c3l6/N28WsD6cR0x/C14G/XyCrDzHNT45Vzs7CNaQARjv8N+3BT4IEfl5d/gT51YtQ0iEUAuMwfFIa0VBdTySjA+bkDqBQyfxSh3OMhab93x0o3ITnc6aZOq5js+7WX6PN+oIgCv1ALdcBWqkNC5ax93e/RyiFq+v47vIc22d8dA4KHdDxsk55vjOGZhEyV9Ag4YxXf45fD2JqAtfYT/f0NHy6Dw2NWruWfun9cKRDwk1wtFCojVv49IGfIhNxJAqpgdAMhKGj6QahNJeQnUJOKA/NMNm/cScFixfjD7d+vnHb+97NA7STI6eczMhkAuSvn1rDvi3VbFpRS0rGCYya0IXJZw1BCEG/9z/kjgUuX82bTI/f/p6zTzyDRVkr+ebZBK/85RtO/+lwhvbt3+qcnYKCz/+9nlidzbjTe5HXq22ztobneAug+/1UZG8ks6xZk7i+2mWw4+A/UNun2ubrka5sElAAxq37MzT77bNpnaTnJdcTjHz3DOr/zTgipHxfUHTMONVJ1FeXgQFrl64gtUuFp/LXPR6VXgMHEgpHkNCUaM0wTIwUk3BKY0RPjw7rr9i1kpeCc7l/1LVMG9l+uvJtxdtY9fTL9D1vMtMnNo/Cl17SMoJHSonjONh2gniihkS8hsrCvcxa8BHxeApnnNfn4KoB6BrpyubjVyA31jGguAdDUgZSr8ewpOVF1pgOsrI/ffdGKTUDuKZJf8frVIyy/Vgvv8QvDqjPMiNw7APsLO54IPIllmCbAzHtdbhuGVb/W7B2PkM360v23zWKl6IPYes+lC68nyE8k40hwPCWmqFx9M6R6Njs/Pgzep9xWofnbITj5GHV7kdFGxChMMr1cqSUrt+O4TPR/T7MUIBQVrr3DNavRfqaZ/9pl+Xj0zRkwqFm+xbk8jSiXTZhRLbwTbyB6pCfe5btRNcE5/XOZmCG55TsujbBh/vhVzZzj/oD47t6nbFpHp5m4YEecW7Z4+cPidM4MSC43PoCxxlEzx6TD3nspldeIfbXRyDJz/PcL35JKC2X037yQwqStOadxnf41qQWR++AQ+T/BNxEUpPi09nz4ov4Pv2cqn69GfP8DALZOTjRKNHCvWiB5vte9O1KeuzfyZaxJ6BJiV5UjJ5fja6H8Yx0LmAllxJH+tkV7cI6u46S+DZcErjUgB5n80FWhvmF85vWe3URjN5Ujr3tSxwjSZOvQJMgpEJP+rQfiAIgavhZXRojk3LPVwTPF6O81DPHmu2YUCfd4DEM3/rccnotqWHl3HJ2WZu57uIhjOh1OSyYQbA2gfPDX/PhtZ9xxi2PkfnrdN772zI+e2wTlVfXMHnsuDbrbguNmpK9myvYvrKMzIIIE6e33fd45b1gh7Ln15HYXEUm3jfxyPAqjEQu7xZEuPvLZWw9tTmx54GvnVKK7Qvfo75sN8FhZzEwWWbNiS8xYvTREC2H9B4k7ulHzaZ1fPDYKi68Yxx6eybn/2EcEVK+J3gOVofuOJdtW0VJTRV+wyAjHGFMn5EtHGlLivcQ6Q7zV21Cyu0tjs0KfMZP7/gtSoFhfLdH25Dw1O0hX8cddm3M6/FCgY7LaZqGz+fD5/MRDqcABbz+r4+pI5tuRtvssI04/pQz+Fp9BsXw1LR/EQy2PNe+7WuoefRi6u7/BRPOuTq59c/s+fGPaZjX3AHHH/s7PcaOpv7BtdhdgmzYW8OQ7i3VWbUUEA0OIPdXb2EaBsqxMHQTQwheX3YsgxY8ycSaLwgN9FOvpSAdjxVYOhLlKKQrwXIhqnBdRYk1kDo3m6IvKujdvqzXClZdLrUvvUfajy/H8PlosKp56U8tHZDPuvTXDDj7eF5+650W23eWVjLqxAmUfvsNzio/Tk4RzuifQNk8qoOecPbPOs8B+G+rqkmJughAky6blDfLz1g9i+2rP2Ek8OmWSgaMtsgIdG6Gd3Gf47hlz2oAUp3RTDvt9kMcAdGKCnZ98QXiz/cQAAxXEknLI6Z81JSs4s0//oozb7mbAROHt3n8rl272Lt3L5MmTWoOEVUctiZFijhu4aEJ9/4TuPGkJsVnEKvzvp/c6dMJZHuzeSMUInXAwBbHSNdr0/SXHz9sE1Yjrn73PpZXf8CaaxYhhMCVLvsa9hGzY4R9YSJmhMBlAXy6r8NzXPryJVRbVXx02QdI2+bif33F2mIH+72trcqGJdxIkPJDpIN46NqxzB9bwsJPdhCfW8IHr/6FjPI9VKekcfSns5l/69X0/9cXfLTiRE57+kOu+e1Unnt4NsufLaeqai7TTzrhsO7Fuq/2YZga5/6qYw8nTQhcqUhsrmraVpL9Aj/68zcAvPjka0BLIV4p0JJ9/IZ1Kxk62wtoYNXdJJRBzQ+/ZUT3ZCBAxIuSc4WP9LwwlTvqWPin15n4o8n4urfmG/pfxhEh5XuDPKSI4roJLn1uJ5ZsHBRq8em7OalvBYYuMDRBPN6DnMKjuOy8C1FKIB0Xx3V494MPKY/D3b//PehGh/TyHaE+6UcQ9rdPAQ+wf38p1cEI68ptnB270ZOOlzoCU9fw6Ro+3WBTVTWvFpYR0DWG6Iqe6eneAKLgult/csj2SMtFIvG3wf0Sa/AGXF+opQ+K2bVri79HnXIiQtOw0/1k7I4x997lbLq0D2G/wYi+GXTNCmNraUg9iEgKd+IAvoSrxp1E8Z7tsOYLrr98LEao43vjYRJz7nmWyqrOq3CzrupNxQubvTQIwHE//xH9VhyDa9nez3b45JVHqNxbiFKKjGCAqlicfl3z2LmvlNqqWhoqd9PwSQVuVhW9briILHSWlsEfap7m9KmPIJXisfVFrKhpQGo6roJqy+X+zCs5umE7qULhuJLPtAl8vDGNGfd+yfDBWfz2+P4c1S29w/Z/sNkTmtOtOE+eekqHZRddex2RJUswHKfpu6g85iichjL65mVx/L0PMvNvL7Fl8Wd89MidjD3rxxx/+Vmt6pkxYwYAmf50hkzwBJlYZS5ar5JO3/fSmlIeXX8JWl13wn/6EkOIpp8p8NY1gS4Eq8KQ0SMFv64ljS2gIzDwBigdMETzdl2ALgQGAqeqnvQCkymf70KvjBEccDpiWQ0lde95jtSGR5kvdB1heA7Vge3VuEbgOwsoADEngUazAKJrOt1TvsNAmEwOpfv96H4/I8fmsLjQ4d4RPZKpFLxXVylFbU0CXtlNWsahTX3Hjcyj4fWv2UEX9GAXKoenYwwciD+UwsmPv8O3L/6V3g/8mwV/uYWT732On901nX/+9V32vJ3HM/ve5brLz2kxkauL1vPWh19w7NEjGdizd6vznX7jcPzBjvtHITwCzpyfjqL0n6sAyCu7itrTL0bMupVIQz035rTsdw7UpOTO9pyflw66ndRNb1I66fdM7t46UlEAoVQ/ZryWslXb2X7ynzHy8giOHElw9CjSzzuvKdnr/yqOCCnfEyyrHOegxFYHI27HsaSPm46NcuLQwdzy+ir8hs2mUh1HajhSsK9+BDCCu68c1eJY0zTZuGY1juMgXcmkkzseJNpD1GqMmmk/TBlgQ32C1yec5P2xu6rDspjeoD5PgdhVT+/MPjyUmY7uP/Tr51oOtnDaDMtONNQgAN9BdPWhK6+gOC3M27U2r44/gV3JY3/+y/G8/dE2onOKKX51BwKXzd3i/OiGsWgyjtLa77iUHUMpge7vvH+FY4Ohd352HhhcgNBWIkyvvWY4QM/JY1uU+eL1J/hmwet8s+B1pG6gFfRj275iEILozhJK/1mLJoPUjFqMEbyKVGB/rAeRoEHPZN6avx3T0p7/xYYSfrj0NObkRvj8F1OatgcLq7h/3lbWbijj4jWlZOZHuOqYXtw0unsTQdqB+GhfGYggX44f2GrfwRC7dhHLykI79ljSBg2k3yWXULVlE4v/eAeZfTz/pDN/fgXWDRfxjyvPZ/Xn77UppASVj5iwyHSbBUehOXSKmCaJtbu3sbpsOONTLXQ0bKmIKYktvailxt+2hI2sBF+Oj6EhPy7gCI+lt+knwEUll7RYxrsY1OeajKvsSr7tR6RqCM3A3qd57dV0xEHkfX27HEtFv8pOX0tbiNpxnMNgqW0LUkrWOmvJpzkEXQqF6hrk6mH5rYSobcW1zGI3egcElgciO8/HjiogZzzn/P2KFvsmXvkrPli9mJ4fLKL0xq3k5Pfn13dewuPPvIm1MJdH9r/OT28+n4Dfz/xlS1n86h7C0Qw+nb+JRVPWcMUFZ7JjleeAPmBCLt0Hd6zBBbCFxYa6IjYs/BJ3qIO1vwGFQ7razsmnpfP+zscx92hsXSEQCDRNcJlP8H5wKqd/voajMiejZR4Lmb1h0h2Axbwv3gAUVszh5OIN9IlXkS+qsRosLH8aRu8+FNz8D2KrVhFdtYqyRx6l6sWX6PXO2xgZ7Wdg/2/HESHle4KUCQoLX2TggD+0WyaW8EJMCzJSGdNnGPN/0zp77W9fnMNbG+pbbR84cjQDR3Y2SLN9NAkp7eTjaYTRPQuK4YFsjT7hdC8XIgqJwJYSy3WxXYnjugzJTKd/Tha1sQTHPDiffbafKwtjjN3yJaYmvDBqTWBqGj69URPjLWtKYaRy2fXGu1jSZZ9WiZsqMHQDuW4TPQvyKaiJtmjbN48+xMbSQraNmtwiH0tK0OSaiwZTe1Y/qmM2Pf7e1aMveQKCQFrtLipnXw/RckTFTjACoJugG4RK9uMKA6OD/EsHw3EERhtJHjuCUmYrRtIDMe2aX1C2dQeaYRA1BHc7CXJllLPCHxEILCax50xK+ryKI/cfWGsTX0NbGJDrCXlb9rd8r6YUZDDlsgmUxyzuXbCNj5cV8ehb6/j7p5uZMiqPP0zpT8+UZqGtf8gPMXhozWYeO/4oOoJwHOjbh3H3NvOCqKRpwzwg23RloZeTyUmU8ukTrzDtxpb8Er+86Rbc2gT+vunNdWsuodRDZ51uhJ08771XDWFAfvsCVlXcZvTdn9MnDu9dOKrT9Tdi0bYyzt1bRMrlg+nZJxNlWa2yiyulULaDshykZVP44Cr2n374JGIHIuHGMbX/zCnTcj1/moJAM6NO3HXRlWxTy5OMhPe4aw6BBX9+izVFnuBQ1tD2JOC42/7K7s9PZfE/fs/0+19D13VuvuESXvvwE8o+zeKxP71LoKtCX5eDTLcZc1UXFn65noa5Xfnb4nfxx7z69y1Yy0erVjL0+B7kTBhEpMAztZWv3cmqVxcz7upjSB/Yk0pfDbu13axqmOn52XTR0FCU6zX4jCourN7dFM6skv/dJCsJ64q73GFsy5lOOnVoyTxLNTJAtR0kzRenOpKC7dvFHfs+opoe1CQ8zW9ezxRSTjqJlJO8yV988xZ2nn020UWLSD399EPex/9WHBFSvkfkZE/rcH/U8qi3Ax3kIYnZLkYn8sB8V0Rtb8A/lLmnIZmwb0qvAnpF2s+seiCCER2ZzA9yakaKJ8xIRUJK6h2FJb0ZqyUVtlLYCqqkwTc4vLAmwnqxl73GgbwIIQonTYIlGyl4+3LCVgJN16lsqCNV08gbPAxdtr5XqUGT1KBJgx4i7DYLOBouGd+80TSc16elIKREKAmOTUUXH4cT/FjoD1CUpuMu+wxDeOYCXQik0BBCa96mCRAeiV0P5cMxdD79+n1yM7oyZmhLcrm+Jx9D35OPAcCWin1fzec6fkeuKsYVii3uk2x9vTeQwdrnT0c3PdNVcEz7WrwluzxNWK/Mtv2LsoI+Hj1lCI+cPJiX1+1j6b82MHBmOTNnllPTM8jYU3oysn8K2xrioIWwOpHJWTgOwmw5cMrks9IOEASzeuSS3WscZbuWsWHea5x8/cUt/K3MnBBmTst2KyU6ZHc+GE7yvHob2qFGbKuKcua/vZD5QbnfTf2uJwfzxqiRgwUU8Bw8hc8En4lOEBswOukT1B4M3cVw/rM6ognvO5nYrfl9jDsuRjtJGV238Z62/xyk4/LN/e+zpigT025gSH+XkZcd12bZjNweLBzfk9DSTS22XzL9dOZ1XcqyFxNolX58x1Rzw6UXYBoGR48cxcsvPE7NIs/pNejWkpWlUVIZYc/nDny+jrBTSci0qXAykHou2x9ez8W36dRQg18LsOCalky9N7x4FP/IUFz7i3UcjNUPT0Aoxd5pY1lZVsdtH69n66YK0rqGiZbW43friGsCo6vDYv9U0u72Eq7uun4GaDDiupYkk06pJ6D7Bx5aM/nfjCNCyveEbjf6gC9Z13dM0mitgaF7ESHJ9Rrbxy/357Frpcb7eR94/hG64S19PoRh0K1qF5cMSvD1N+vQND+65kfT/Rh6AF33o+sBdD2AYfgxjBCGEUj+gvh8IUwzhM/nb4r+ORiNQkpKsOPwz4bk7DPjMMwfB8647rvlaNLayGZ8MH7/z0V8VVzDoHtPYe0jL5ITy+CGO3+G7drE7TjznnyGFXX11IQjhKw4tm0RNkx6DxjMCsPE7CD78NlTnibPcHj5mPOa26gUSAeFIHIAkdrXs/5MVL7H4RjRXh7Sky2Z4fYJ/A6CUIo/pH/FN3WrWLXd64yv33wFAS3AMQMmM3RIS02ZqQmeYgeKfWQOeImqlU9ghpcy+oJ+aKRiJyzseJwN83Zgl7YOtaxP2Hy8ppjb31kLwCMXj+q4fUJwYk46uw7McVNlse2ZTSxK1djXP0CXbvUc06Njdbodi2HEYqiDtQhJYUEc8G4apo8rH7ibx394I/G6Pexdt53eow7RaSvRih21IzQKDYbefnd4+utLSdQmuPfy0Vw+rFun6z4QjXfNPQz/Eh061Kx1Bgk3gXEYSR4bUdVQRXVDNY7jUFbvmUsChucbZllRok7MSyTaBuykKiVR04BlWfgOetZWTS0f3fwqJcEBdNOKGH/lSAqmjOiwPaJ/b7KW7MZ1bPQDfO6mjB9Pnx7FROPxFj4o37z9DwY9+gTRlBChmx5g1LlnYvgNHMtl19x1VK7eRuHOOK6rCLh1ZAYS7KUbnz64gDy9D0Vqaas2pGo+HCWoi1eTEkhvsS+gQZ+kg/p5zyxC1XoTuZqSBoJpfrrnRRCaYMuGcvzdmt+1mAoScqoJpLWcGLo1ns+dmdf5LOL/jTgipHzP0Hpmg+uiHBccF1yJsl2I2wQSCbo1GPiQBBuK0aSL5rroroMuXTTpknlGDbGxDrHYYrQOKPA7gpQCJXWk0lHKQCkDMFHKR3FtHKHAp3fcqdU7DihJin543BL3XD6K3766mgmPzuOVq8czLq9jkrSELQkkO3XLtfHpBpqm4df8+E0/03/1a1b+/i66jxrDRddd3+LY5z/8DJP2hZS40gnqB+VLEgJ0s5VxpLy8CgIhnnnsFeKJGK50kMpFKtmUj6UqUURBl/6c+4MzqCippcHwM6Ve8O+pvXGki6tcHCnRUSjprTvSCzNWyqU6EePKva+T4nqmtpAM8K+6lwBYtmQFTw+Z0eoaulFMERDe/i01mh8NjakXPtaizJIFVxIWLbVddTGb4X/8vOnvB88fwZgeHdu9t+6q5vP7V9AFQbxvmF/8yssI+fmKYso+3sEZy6OccdoIenVvm4Z98bXXoW/ZTKS8giAQDbR8d1SSZl3QelAeM+08Fr75GF88O4OrH/oTZgcJBeHwNCluMmlhR8kDYwkXUnzfWUCB5hBetx3tw8FQSnkOuZ3062gPtrQwDsPcUx+v5/aZtzO/YX6rfWnBNCwrypy54/go4OXp6jXXy7PUaPaQrkbffS4XAoVvFfLMW4VAc5ojIcBVQHAAw9b9i9y67dR/I9gkPI2ixycvAA2h6yjHAtemd9Kp//R/jkOkZaOAhCupF55v0IEYuT3Obe9UsXd4NpOefptIl2Yh3fDp9Dt1JJw6kgkHHNOwr5xP7/mCOsvAdOsYmiSMbIRUkiVWhTfBtOMHB/cg8ByslVK4tqRf3wzm/OiYVvdwwh2f0esAH6FIwKY22vqdUI7XN6k2tMH/SzgipHxP8PXsSeTEE8m97dYOy43pcC+sePl03LLdTL54PVK62HYMx4m1WjpuHNeJ4zhxb92N4zoJXDfetK9xXcoEUsZRMobm24avbeb4Foi6LkLZbTq0doTLh+UTvsbgV6+s5No3VrL65ikdRi7EHBd/Y8I9124lPEnXRQmtzZDrhITStC7ELYtAG6r1uDIIttN+KV127FjAmjUL2bmzirq6VPz+ibjxvQSMFHRNRxf+ZFSDg+16/kSFlVv5xxOPeW0fOxXh+EgJd848sKveM7ucNe427hxxLgBvzH6Re/Y9xAldp7R5jKYHwIW03JEU1a9FteF7IpREHHSdVbHmh3zBmAIuGn/oaI+iomaflVPP6tf07E8bl094fylrPqoiYrZ+Dkopll9+BWnLlwNQPX4cZq9eDPnZz1qUs5I+WU4bYaujTp3Ewjcfo7Z0La/d/VeuvP+OdtuplDisEOTdlZ5wZHUwFnQN+4gmMyF/V9jJwcbfyW/GcSQ6ojnPwHc+r0XA7JyQUl5bzsnvnIyjOehS5/Yht2MYBlJKAkaAacOnUVtThGkmuFm9zifmGZyRWgkoEvFS6hvWoepGk5bTBzdH0K2ilsxggLTh3b2wfSm9KKBoA2rpp/ScPB7kWJASlMdki2xcSnAcpG0jq8sRmTms0XdzQlZvjxZfKmqro5gS1oYFvgxP6BUChpSsoz4Q5eTX56B3oCE7EOFuWVzwxA8AmDX9KJyDNBuVZRuo0DXO1dJJSWmt3Wh8SjGpEK6if3bbJnNNgDzAJBprkASN1p2uv7/n4F7x72fJ+cUtnbqG/0YcEVK+L2haCybJ74oDs6Bqmo7fH8F/iEicw8Gqr67GH11xyHIxV6LRftbmjnBu/1zWnNCH5z/dyu/mb+XeKQPaLRt3JAGtWZOSdlAocnV1Fe+NmcKH8ShvvP0Rfs0Lfz6zZz4+16a32sr9Xz9F73CAkT0vJSOYhVSSetsiJVEBiR3U1BzFpk1fs3PXJkpKaknEdRxHYNt+dN0mP99PPA6WlcJdf7itQ8Fs9+ZiVq1YS3ZeF16LxjD9nb9HDY43SIcOEMT65vWDfXBvySNEvkpl2qRzeGLmI1RVl9I1+yP6BL36v657k7D2Dbjwytt/xfQHCATDhIIp6DJOrVPHgj1FRPwpBA2dojJP4PjpCf349amHtnkrpVjxxR78QLWhyDvID6TRB8EwWptZilevJpwUUHp+vYDBWW1rWmrKPRt8VWV5q33BSIDL7nuSV+78CWU7F2JbNqavHW3KYZp7dpS7yWtoXxjIDPoo3V2L7UrMw3CePhCJpAnE30nzTaMZ6j8VUiyqCYlDJxm84q9XsCVtC47pcHaXszmt4CTmvvsFCs9UpQvB5g+Wk55VSN8hMJG3GF+1GFWajiYMlFGOFtlLjz7H03/UFJgGa+98By0YZ+hVQ9o4Y8fO1W2hF9BIHek6ktnPrGTY7ijrjACnXTy+qdzSp6cRiSc6LaAcDCVEExUAwKov7uS6vR+BJjim58mtyyuFVZegyK7j/ndWIixJjy5t+3gdzJjl6j588dYRXMGhQ8n59a8offivCF0n62c//Y9C0f9fxREh5fuCEP9HhBSlHIT6z2zUHSHuWpid+BBi0kVT301IAfjdpH58vqWMlz/byt6KKE+dOZyQr/XAEnMlgeSgUGxXIJyW114nNPandoHULuj1ldgISiIZzKyS9BR+dovuzFU2Qxv+TsWGj6g4sA0mbNs2nkcXeUnlAoEY2dkm3br6MUyDPr1HM2zYSRiGj8cff5yqqqpDao56DuxKz4Get774dCHBQOefVb3jzaZCB9j5xw0/hlFLh7BK38Cdu+/mzt13ezsETE1An6RLkL/2C4yAonZvmJJP5raoNwisU7N556uPqSh4Av/8CkTcG5jTgp3rAv726FL8xZ4QFXEFmRkthcXte7fx1ojnefGDqlachbpjY96kIwD99RPRlUBXGsJU9I44XGIIhNJwHEWfaYoK82VmvfM+SaYRhDAAg/r9A/GnH4Mb13ji+kfpPuxEdENDCIGmez/PzKOxc8N6NEOiaQKhKfqPGkOXnD7sWL+YyrJdCM1FCBeEpK9vK3AM783bRmawAZ+h49SVkZouCAZ9niN7QwwcxUsbdzI0I0gXv5/0gJ+wz4ffMDp0um1EPCnI+TqpSWlysO2geEO8np0lWyks30BF+RpqSivZvXcIUjaaOQXHGv1QKG5/+HaEEPgrqjGry5F+2RT1JYQgL1ZDV9IIuHmkuDuYm/kRbiiFtKiNDwOpeW2KV3kaAqUEklqUUYOQGkIprPIUckZ6+uB9r6wgQ+VQK6o7db0HYvm83dizdxM+uy/Dx+fz7ZztmLOLKO0e4uTrx1BdEWX106sYFlWs7xFk6g9HtTjeV+V96VuffBPlOuA2ampU81J6qamVR+4CSlFs20QzDPpurmbFMJefL34GqSRL936IDjygDSGxO8rXz92O60qkY5FftYSu1i6GEmVFXV9WryjBFzJYuKqE17Z/jJ6oYdyJF5GTm8fWrZvZr1zK99axZ84CEIpqPYfaYNum1i7XXedl+v7rIyjX/Z/UqBwRUr4vaP+HhBScQ6Zq/09QZdUDCstN4OvA3yQuQafzYZ4HQ9c1vrz6KM5/awXzlhYxcmMpZ4zvzi1H9aJX6gGZjSurSY3XUnTnwwzYtRkptrL6wQaEaSJMHxVSMU5P5bLJ47jkhKkAfLh8JdfXCnZneHbozWIIkX5PEKTGM1MJzym2fsu1RMIax07KY/iwSeTmDm13puI4bXO1dASZJATrLOrtpCbFaKmav/fM+/lqwxyU47K1eAMfyC8BWL4vSLRmOM9c81bzOaUkcVGUaKyeumgNdQ3VFFdWsrpmFdr+V7k5I5UXjCoSuNxyYn+uOKrnIdv12lsbMbd4mpcGTdFjStdW9ylSEKGiuogJgYlk+NKT9OjJNPXV1QRXLMY/eDDKb+IoB1e6vBPaRBWKa9xclHIxhEOQOAT8KJEA4aLwBAlwKdt+EZqRivIlEEKjZHtNkx/EwajddSwrdzX/vX31a3QdWoYMfJS8UQe03dcdQxvPk6sBdnR4L363thSZ19pZXEgJKIRKGtwUiOTf4DlFS00Dw2Ta2m2krIQAggAQUAIf4EPgEwK/AJ8QKEehDfazJrabY2d/zDC1B3CxnQS6KiGkF5PmqwbABLINnZxuisKKo/Gbppd/RkliTiVodfgDfpRSBDfXY9bEifVLSd4/hVSKgJZC2AmRqqURK9qNX5ikkMKp2kn4GocKDWgA67PpJAJxVJ6BsQtCmqfNdaTFntXLSXG2NzvrZh3+MKNm7aZACnhnB8/Neo+T6oezw6xgyJ4s5t7zDfkJRY6Cfad059SprQnbtveczuCNr+D8rX3Kh7bQFe+7BdidEuXLzU+AEAhN49hojKMrFiLFYryuT0OhkaWapz5B4U00rKjD+mgtd5LkJHp2Bwe+W2muxkdvNk/yMuO7UK7bwmlcWhbxdevJuPBCZEMDFU89TcYPLm5FVvnfjiNCyvcEIYRnZ/0PIXHR1P+9x6bHtnBXV5sF84Z4tmPAVQKJx5krk+vn1vZnzPYx3LJyDUponjlLaySl8n7igJ+me4kBhaah6zqapmMoOLZSY2C3fPYW11H4yR5+8eluZIrJCT27kJsSwG8pAjVF1H79HsOlhZAu2ooVTZP1XOAhoGTA44CnUp4+djQnWwka4hamaTF68SY+LdnP38c3R/Eopfhys6Bnr6FMOvaGQ96XqirPX+Suv81A171r0HUdw9AxdB3T0DEMA9PQ8RkmpqlTG0znXQUTNn+OhsLQPGZSXYCh6ZiagaGZTctVlUUARA6KmOiR05src7wZ1dS/j4N0+EXXa3k9+lar8VnTNILBCMFghMwunt18OLB4VZyV++GHA3NZvqWe5atLeGVjMf/8ZgcpYUG+H/59wRjMQICU1BC+A8w2u+fuIwWBeWw2Pz53IGltJEILpQahGm6deiuDstswH13depM253K+LF7DuMu+OOT9B3jm67fpNbaa06+97JBllVRIqZCu4oXfv+X5b2lzMGInMnzMzaRldgc0UAbHoXHZKQLbhYTtUlVRx6t/+Bl9jzmHQcdPIJ6wiCdsGmyb9FSNeseh1nGocVzirvTC5b2zeon5lJcGQymPN8hzBIV19TEWpnfl7GAQv9SI2S5RVxJXigQKSylqUFgoLAWupmhI19gfSUeKAoZGZ6KUhqGZWCqHBn0keqAX2Rn96J03kF2bXqIh+gz33nJNh2aBf/78xxAM86t7n263zKMXT2eMbzT9tbFUG+Xk/+pkEtEGjHCAssWbaVhdSaDcR3BPhKhejzolTGrPrpQ9tYYMN4dqVUa6yEaEdIb8pGPqhbagA2tyfRgJyYnVw3kpeyZvZM7iVvMfHL1WsT4gGHf9KIZ1a9vfK3HJ+cyfNZLTbhlNdl5Ksj8S3kRDSwrQmqd5E8Jj+tWEYMynqxglfMw4YxiDgd8cVO87O8q4aXcRT3Xvxjn9vEnQ7q1r2fH1W8Qq9zHbORp/nUZeaoCcVD97d23nl8ZbzA1P49MaL2/ah+ensO3tfRRbOfTOKUVbtJD8fQvYNvavdDnjGOySYnzd84nVplH7yWfJG6KjpaWhhTvDdv3fhSNCyveGQ8+oq7buY9HT81GOg66Dpgl0Q6Dpmrc0NBLHeCRdy+Y/jaaZaNIkInVCqWGE7kPTA+imD033oZl+dMOPMH3ohh/NDKAZPnRfAK0dW+2kjHRQZcS7XIztxpHSRikHqRxU088lXmhgJhT5g3rjuC7SlbiyeamkREqJcl2U43gcGMrbbkvPKU7FE+AkyKtJoZs0EK6G7oIRU4jSKiqBSWQQzxnM4DW/bWqj5z1v4yYSrF28lPDPbiLnIEbGoM9P0OcN9lMDu/mqoStSyiZtiOsmEEKh651LKifR0JDE6qpAukn1sEQgk7NnmZxXeT9dKJhyDgB37Gs70+rBMCybDCDcTsyyEAIr6L1HjxY/B0EoFpsY+eJI3jzzTQZkDGh3cGpkiLWVy6UjutKwYQPRXaUYlsZD7z9BZryWqie8st9268/pcz5sPtbQwFLkpPrbFFAA4o7nVBo8jKSEUrloh2Vj73xZoXn8M7oBMp6Lqu/KSSf/rgUHy4Ew8YI1UgCftIm4DfTISGHcsL5tlv8u+Of8hSx04c6xPUg/jMHmwgUvs9bOp9/4fwIwMrM/gTZCf3dIWpAXtgspEYfw1ZBIVFBQPzDOwIumYYYD+DM8bUnP0ybAaWBHE8iE3bQdIPj7NGJlNeT3mkTxX75tobE6ZLPq67HXrMI3biJ+BTJkMvWGwWw56gSKpuXQ3+rLa9rtPN7bZkrPn3F2t7Y5VQB69EljlRmmMCromd1xBGEjlFKUhDUWdJBvyEqa7PwHRFz17D+cnv29lAwHU67N+9c7DC1ew0V3fkhNdRW2lSArJ48Rze4zRN+vYfcds3FcnbL3F6NcAewBIDS8D+lX3oBbW0vK8cf/T1LkHxFSvk8cov/Y9tFSdtbnkGaXItE8zUVy2JNoSKHTLxnRVuM82HRcBUD14TWlQN3AwBNbRxrl+v3YdgpnjPpLh8e/vOO3JEIOv7rwvA7LdYTP3p3P4jVzuPkvxxEINne6UkosR2FZDq//4nMG+OItjhNCYPh8GD4fTlI9+od5u1jxYUnTLElogBAYpsZVJ/ViZjzEzN1fM72317HZST4YQ+/coBr3Z6CZfh7+9fVt7ldKYbuKhOOScCQxy2H00qOo813C9GNvwVFeqKurFLaUONLGljaWazWtryjczj9LINds35z33Bkz+GrT5zi2RWm8jIXWakpi+7ngowuSIZAGvdJ68e70d1sc50s6kjYkEqT/7V4e/WpOi/0rTppOjxGjqPx8NgPWLWLO0VPp/o+/s89JR9gSEGz9dj+c3baTc9yJARD0dZ43x1WuF71yGDjMvIFJaAjha1dAORArZy1k/ivPeOf6D0N/D0ZTdM/h5tWyy6gWwzh7gzd4XpX6CQ+MPbdVMVfGkVI/pHOlUqrDMo3+LKlD8hl0TWsn0UaYIT+EWgpLvkgIX6RR8Bd0VkqxN61l/4xqAPSPPyNVpZNpbSO6TmPu0X9haDX03fkIkMXe7Ci9Rncs+Pfvk8EqoGhvHUzssGgT3GT/HOygyVVRz5zjup0z3evSwsZ73mnpbfudhM75MYPO+iHOtpUYvUcQXziTsgfvw6msImtCgPBZrdNB/C/hiJDyfaETjrNO3EKTNpc/+4MOSp0JeAO561is/+tifPE4vX4+DOlYSDuOdBK4joV0Et421/KWMoF0LXZV/I2E2Ndm7Uo5nQrftCzw+f4z81UikQAFvoNI3TRNI+CDgE9Hky6G2X57vt69nzOAXXuimN1SGNw7w1PzS0VdPE5J7Yu8vVlgpk/i9p09SN3wC4QWQFIDfli6ZCV7NjyHYRieucY0vXWfD9MwMX0mhulDtxsQHRDXCSHwGQKfoeHR4PlZqzn4hI/UQ2SUbsQ3G54E4NLZNzNQahgIAsLgrsn30bevN1gMzB/GwPzmdAn76vdxy9xb2Fa9DYHAkhZbq7Yye/dsTj4gCkEmSe1iH31Gt0YB5fkZRMvKKd6zl+lXXkZKago7jpnAxgcepc/SL9m/YSvLagoIKG9A63dM+6RSjZoU/2Hw5jhSHpYmxarPwIqVdrr84eKzp15jy6LZOIkyMntMYNAxhyIEODzYSXOvr40IqI7wz7HTWFe5i2CgGzds2keFA7vrSqmzaugeySPN771x0q3HdTsRatxGSPqBcBOeMKQfZjtb4RCPNvrRTOKbylAOxGsKaCQecd10fGITEX0DKeMuhGfnolRziG733ufykwkd08RnZwSICUVFabTDcgfC0ARdo4qRbvvD4vKiagjCja+s4A8ZQUxT88y4+gFL3UvxYeqCcyprmaRKWTXzj2yvD7OjvgtBTWtKROjdI5HkjxGweAMJR5F5/lUMrZmPIWr43zPwtMQRIeX7Qic6Yzdho3XAkHogNE1D8wUwnAACH6EuBYc+KIk9nz6N1g4DpVIuQhy6c9qzxwRMduxYhKbpaJpBVlZvQqHm2cL+ylos2yHgNwj4TEJ+XwumWythIZTRoUOqFAaGr/39gyyvQ03vncUvTh7CZUObybbm79rITfMWY9mQUuvSXz+OuFyBUDboMdxEiLr6CKX7d3l+NB3AD4ia4g7LHAxduGha5z+xqb5U3nFdjg/k4dcDuMrlvXgRf15wJ32X/hVNaM0/BLrQ0ITOJCE4LjKABuXycp3HVPvKtw8T272QiD+VvPTeWEkVdvC4Kbj3PQTA4KNbTzH7DOtL4Pe3UXPWl9j/+hdyyE9BCxIXitNPbu2g2Ih4kicmaByeJuXwzD1QuLpzprMDcXDIZ3tYP/cVALoNnMolf/rlYZ/nUHBchRCyXbbn9pCbNojctEEAhLcUMTPam5nLvEnGeONLHhg6nCFd+uLKKFIeWkujZMeaFMfyBALd/G6Z1BshZAMq7lDz7Nt4nsSqyRxll8WJVfbB9MXQfAkCmfvRwwqhEoROOoqG1+4mGvF8a256airxWJzXHt/EsMnHMX7ioYVHIbyM1InDJELTkjnI2sPQ/pl8XFhKen4EGXNpiDtIqXCTEyN1wFJJRQ/ZldO1CPmrn+eR2PXMd3LJErVNfEZefFHL9RoVJlf4mOb3fOyu3rWLXr16HdZ1/DfhiJDyfeJQmhTLQRyOERcQtkQewLuwa/UK1n45i8GTT6Df+LZ5CBQ2oh0GSk+T0vnO6cUXZzWtd8mMcvPPPDPU8s17+PDV51rJZp4TroYUGqaSGId4BV1hEJs3l32XPA66jjAMhGk0LbOjHv/DO9dNJi2lpcYikSTfEkowttrHSz+4CrgKgOrSKM/+7GImnnkMx19xAVJKbMvCiseSyziWZWFbCSzL4q3HH0fPa3+QPhhKKQzNResgs/LBSKt3WbCniKiopsrXFefk+9C2PcL6WAlrYyWeUyYeEZTnyKxw8To3N7kvC6jWYE1DIct3vt1U9+l9fwhAXrd8VnfJIqsNLpJGpHfNYnX+YBpSJxDSggy9uBeDhubi7yBrddyNI5TAPIzr3V9WSCzh57HHft3kd61pIvlLrusCPel4DSfgT+s4k3jbUJ2aJDRi0KSx7Fy1mp4jhh0W38qhsFtpqO/IsdKIp4f2ZWN1IaYm+O0ek6VOL05aVc2SMUUs3WeQ4Ts0KZ86hCbFqfMETtXgYtVblNfG2V8Xp7TBojRmUWbZlNkuphD8YepA9HauKaC+Ji4HEd2m47nCaskBWUORiukvJOeuixBtaGx05SL05ncpEAxwza9vOeS1NV+jwlDg62SIfSM06ICjGgJ+r63/umAUR6V3hp/qNOB+lFKs+cc8sjSNZT89o8MjLvzrx5TU+rjrrruYMWMGM2fO5IYbbmiTsPJ/Af+bV/3/C3RGk2K56IcppGiuRIa8x7ji0w+ZO+NfAGz59hv6TzyGU358M4Fwy49JCrtDTYqmHXo2/Ktf/YzCwrUIzaN4/+yzudgHkCbuK6tECMgZejSRcBjbcbFtG9t2sB0Hx7Ep3lRBlt1+jiClFFIz0KSFVVaLcqWXRsBVKFdiVSsa3ch2bVnDiNETEJrG8n1beXLpWyyr+giER7N+MFOtFUsACl/QUzFrmoY/EMAfaNtHRVk2utE51k4A102qzA9j0K7espmeQEg1EEpsg5kXctfdNZ0+vglSghPHStTy+MfX8lxiL/g9DVfI9FFx6jSY9Wm7h4ciIXzHXsqO0i6k2SVMOf6EQ/o5JGQCQ/kOi2zKcXPArSEjI5ScjcpkRA44jmoy20npIqWDqSWwO6lpbIFORv6bgSzseDlznvU0TZMuuYWJ55zUqWNLiwp5+qmnktE8B+7xzLwCxdyjTgMDBs1ahKkUhlL4UJgoLwRZQF+/weMnHN3ueUZkDWBElucXNCpzL//evpwZ2/I47sEVSHkKvSNF/LiN4xpqaynes4veg4Z47enApBuNesL9T8wYO5ZuaLU/7CiCCspNwQ+rY/TIbNsgEfKvImzOx3dH56K3GiGlIkXE0DvJktsWonEHDUEofHh16HjCf3soSXhxXGt2VpGS5RDyG4T8OmG/Qcint6sVFkJ0Wla2XRdDF+i6zplnnsnTTz/N0qVLOfro9t+L/2YcEVK+JyhgblEp+udfIYQgaBqcfNQ4/AfkL3FtF+0wMxxrUiGTJGifffwx1uBxTftW1FrkfbOACae0DAFUmo3WTmIwpWSSPKtjpKRkMnjw8U1/L1s2n7Ly5rj/xdtKMYEJI4cxbkB+m3U8dN8i7JJYu+eQjgKh0f3aKfS64tlW++118/nwN3+jJBrgobcqCLz1Pk9d1pWfLr8BISQIyNdOIWqvauUrEW/wzusPdc48oVsN1G5dy9233oVumOimiTBMNMNEMwwSmp9N5KB3ycPUBRo22WoSmr6J4vrXMHWTSHkVwxKlpIZ8TccKw0QYfjTDIM+sBaDKl0uGtZ9KX1ei22ah6340w49uBLxkkqa31JNJJHXdn9Q0JKFp4Avh84VA95EvBXtjURQ6uhCoRAK3Dfr6A1FR4XXUl/7r4k4JHgk3jikPb0Awfdn4pMVVV/2pU+X/9btPUZ3xufiO+Olzz1JTVsv+nfv5+LFfE63rvD9DSVEhrm7QLRIiJaXlpEApT9i6ad8GFuX0IhwOYyWzfScUWICloFBprFYBHu/kOfukdSfXtTFXb0JKjQyfg1DZLaLYGvHuPx+hdPUylK4jXBeV1n6epvqI9y5NNoJcGEqjX9hPbthPTqqfnNQgIVPny1VFXFZVhtOhdtj9Tp7OdbU1pAFOovP3/2Cs3ulRBoRC30GT0sElVRd7bap4cRtfxVoXdDQY/uMhTB3Z2n+rutjTAg759Tstth8ckBUzfPQ2PG1Wbm4uw4YN4+uvv2bkyJGEQp3zb/tvwhEh5XvCv4aN44UTD1DzKQjPWc5RFfvR8D6OmiFdGbj/8B5JWEHM9gQbK6s1yc8nC79tLaQIp10zhKdJOfzXQos2YNWZfPAPL6dKqMrERuc3X23Ft7SIATkpPHLy4BbHSFuiOoigcGxv1qz72x6YzGHHcf6Hx+HYNu//8WV2p27kZys+RQg4Nfen/PKYC+mW2oXjnp2K/yChLNHgdRidFVL8MkFQJqivCSNdG+k6aNJBky5COuwM9uLr7K6I8rLkPEyhi/NwlQHrvToeWvAcWsVO6ts5R6+TyiALMiwvzLyLVUyXly/qVPsswBYCR2g4mrd0hU55WoiiSJi9e15BCJ2Rcz/moqpaTqupZua1p4FpYChBVnGUzHC2R5JnmJT5Lwdg8SOfoBleojfN0NFMHWEk15v+Npgde5+o0cCbs38JeJwTAo8N1hNyPD4Kjea/yxNFTUn3OgMlFcrRqKmsx+c3MX0Gmq6hHYI23iMTPfSJNF0nIy8DM2nWEocRedSYBO64k05m0KjR7Zb7eQd1/OGbZTwT7/wNeX1TMY++sxFMjbz8FPTCPeyw0unzm08Zn+UyKC+FkT2zmDiwgHi999bljz2K8p3b6TWyfb8OO5nY7tg+OUyf2LaJM5Fkw/V3eO8lzbmfO4+K2jrSgCHbnqH87hdJpZ44fkrpgo4koXRu5ma2UkCjpgoA1ZjkUJFiww0EyGsnf0570BSsM13O+nA1Arh1SD6T+zWnFDgqEuLNmih9z+tDatAgbrkkEi6W5VJfk8CYs5+yyrYnXhdv/oKNXXoxdXBu07a2nvb6nVvontKsMTzppJPYsmUL7777Lpdeeulhk0r+v44jQsr3hJWTpxJwHOYPySfoM7l91gJqhEaDpuEKgUSwqVcOtemHHzHjr/fsLLfffjubNm2ipqaGlStXUlNTwzXXXNOq/N3f3kJJNIfUr14haNgETYeg6RI0JbeGjyVUNZgdK171vljPlOyFY+qQOqovmcNbO1z2d7cRk7mU1UWQdpwspdgkCthVmsCyYqxdV0ZuxEfYNIiYOhGfTjTm4OtISIl6goTRjgmmEYZp8u6vz+SOd95gnevj3EF3ccP405uSqtnY+M2DhRRvptJZIUUTgp7DR3HBb//c5v57n/mQudtcdjwwvcV2pTxyL9t1mT/5EXYcdSInPnQrrpNA2XGUYyHtBG60nr3X/IzywYLcP/8N5Vq4ThzpWijXi9BSjoWSzUscm8VFCyip3cuZvU5BOQmUa0HTfoshbgOFPoNifzqVel/AZsvgrgwrzCS0rxzNlQRrE4RrJHF9PyiFrQVgkiekrNmseWR9QniOfUkN1cGW+17DdNanwL1FnyfLdWKAFzAo0fmswkITOHU+Xv7Nkpb3WHO58Ldjyc3v0uZxri2pKW1fY9cajUNH54UUmeTPOJzsy63qSJqFOos7Zng5tk4/pjuPnzqE3z9dzIK9leyTaWypkqwor+eldVH4eA+nlTnkR7K55Fd3HrJex/WerdEBl0pcNfKFdDBgKvmdNCm9uxewYMCd6LFydE0ju34z9f4clNCQrsXokneZ2K2e1C65noeL8MwpmhBeEnMhqK6IwZIoodTDc/49IzWFWTV11CPZHIaPdpa3EFIaEwNOHJRFz4MEoC3Ftcyes59dG8v4uHQhqXkJpOFH6X6k4efqjR4x2+BnN3bYhhl33IFzwH1LTU3lwgsv5OWXX+bzzz/n1FNP/Z/K4XNESPme4GRkcEl6hB75nrbjuctb8xxM+2QNjnl4VPOWgng3T70cDAYZPdqbxR1//PFtt8OW7KkvoHs4wXEDFA0JjQbbJGqZRC1I33siQhogEnjJOkTTUq9LJ7o+TvzUmdTX1FHj7iJLL6NLdohxZV8xJD2NyC17+HzXNk6ZMZbFpzzFo0efyr0Lt/PMh5t48oNNLdpya7UnIOwsb6B3VusZjxv1Zn/6IYQUgJT0bDKCDt0TiluOOaflNWO3Ir9KxJLmnnAno1GU6jDaIW47GG3MGoUQ6AJ0zcDnWLgp6QSyW1PRL3niz4TjOu6Nd9G332mdaxMwY+4tfLmnirvPaps9dCLQip91KnDzbU1/fvn4b+Af7zFgzaqmhGyDE25zVJXromy76SctC2nZyERyPWFz+Ts6QbcM33378Ka0jXlRJFK5KCVRKI/ED4lSkvee+ANRu/Pmm+k/Hs/WtYU4loNjuziOS1VJA1WbdSr317QrpAAEIp0frFRjYrnDGAdkUpOidSIyrt06oIlGvzO47ewhPPTRRj6dt5u/pAa55ycttW6247Jmxz6WbSum/oVtALz04tuMGT+aoYPbJ6lznEOHIMeT9yjQQRmBi+LwI4SEEEy+tO0s1+WVhfD3dzl3dA/+PL59bdAnq/exc8kmfMHOnT9RWU710rlcC1wb8Fp/e1k56bqPZSu2JlmDNeIltQxUfoTo36qOtJBJQ1AjvKaOfSKVM3Ivb7F/d3YmUV8Gzs4N6AX9EAf43EgpiVdXA+Aq1UrY7du3L9OmTeOTTz4hHo9z5pln/s840v5vXOX/H6Azik8HdVi5XgCUgOCuGtY+sJTht48/ZHk7GbJ70/BB/OCc1plJV/xmPmXdw5z6k7Gt9u149lV8W7ujZqURJo0N5etYVL+Ra/ouQzPBDXgD/tSCXpSbGWg758PRl/DbY/py/agCqhMONZZDreVSbzms/6qI1KVVzFpUyA1ntaZSbxJSgp0TJCrdeJuqUEfYBA5iQm0UUgLhztt49Q5IuBKWjU7HA67pWNhtCEXx+lrs515l8TCdaydd0un2ANjyuyd5bETKgjUACMeFpJBi+g8YfAwvmooOnkPDp36opVUot9A9ZVxbMPQgmtN5zWFe9yzyurfM5rv2263M37y3Q5OP4dMIHo6Qklwezqeokg692n9AAOdK1co/oSPceHRvlKnx0NvrSAu07spNQ2fsgO6MHdCdR171o+wEpR/P4LOPZ7D09Ku5+qoL2qzXcZJm1g5CpRs1CkZHmiMl4f9gdFRZxV52ffsiWXBIk7TT4GmX9735NYmw91w0DXRDoOuga4L13xRTryJkpjj0TvmCQaIlAeKMxpX1zdvGAdegUT1+KxzEYJKbFuS2R49n9j0vs6soDe4sAtdCOR53lVh2IWxrYOu080kZEKTg/WVN38uj99xDXWO4dDBIL6f1ZHXChAkEAgHef/99dF3nrP8RkrcjQsr3BMWhOz2pDj8ru3lCd+q/KSKjKs6eWbvocWqvDstbltcBme3MgAwFtKPC7XXND0BKpO1S8sclCCSO0nlm20Qu/tE5dDvemzkYhkFRpCeG3ex9kR3yk30QO+X0vjn8Y9kcNNl2z9ywez2Qwd8/+hptyTqChiJoQFCHgIG3bmj4TcHSlBxmhSYgVII7Vn1ColYjEpNETJ14tA9ri2Bml10kzP3kpWtsLF5NTdimqG43iTKXgBnA7wsR8AUw9ZZRKo3+DEYHmhTLdjE6iMyK2y6ma+MLtnZY3vPcn0lrgNePhZ1fXIymaRhCRxM6xQ1l5IYy6Rruiq3g/KE/ITelT9Oxtmsflu9EW3AzvAirzSNHseuccejZWZhp6Uy+6k6MTkZYCCRKHaaArdRhCQJtwU127Bs/KqR4XlWLFBKNmZFxFYnyGFve34bQBcLQQNfQDG9daC6B6FpE0Af+FDaV2ZT4c9hYVo2xYhM+U8eXJPrz+Qx8ponfb+L3+TBNHUPXcJrMPf+JJkUd9rN87PMtAFw9vG3n9Eb88mXPWbOktJIXb76ahrq2Uy8AOMnItI5m6o2fbIf+QMr5rhTBrWDbCbZ//HuO2uEJEpGMjq83sGszhm2zZWew/TZovUFJ6ussRgfWgQ+i169NJohUJKSLGxA0agaVUtR+8xF91txNSLQzOShawZ6iMBnGPvB7Gm6BJ6h3+9dbJJZ8QWzFMsrenE/03b8TuuAWgCYB5Yy+fVFK0Xti2zS5I0aMwLIsZs6cyahRo+je/dAh5/+v44iQ8j1BqUNrj12lDpsmvMepvajqnUbDc+uoW10GhxBSEglPSPEbbX+4hgJhtN0GrZHMIink9z3qKIZ1yeODN2bzxjPvM27RN0y56/lkPQ72IaKEPi9ajRQO1YvW89CmZd4Aogs03Zv52NWSILDV6IZVCzGpE1MGcWkQUyYxZXgpA8IG1qRchL8nSjOYUQWBWUXJszjA1XwDfPv16wQLPMIuIsAUeG/FjW22TXcFriZwC89Frx2CmCwIizd4+Ol30NHQ0TDQvXWhU+I30TIHA+e3qmv/rn0svP/v9HUtpL+lkGLVlGK/OJP1Y8DNNVhctsXjPEkmqatyFFQUAqsBmFf0DW+cv7DpeFva/7F9esSDj7P4+QdJnfER3WYuQ5NgSNg8ZAxDj+nkbE11ljKtGVJ9F7fKlvDrEtuspaokheoS2+OQSTpQNi2B3gGN0OL2yfhcfHTz/wAhXHKBQK9B/K10IPe/2X4UTGuM48UZ29DYhiaU5xAvFLpI5ssVYOk6TrcQoZ4BdAU6XjkdKJMKKxhh5DfrCMlqxquFjNM3NCXGUDSazlykkmgyxh9HVLC6dBi61jkTYW5WOrqSHYb3uu6hNSmNJrGvNpcS8hsEfbpH2ug3CBgaAUMjrNz/SGhrhGPFiD7Yn6OcOtZljqH/jz+h3yHSLwTMKMd98xsyvvqKrIwMpC1xbBfXlri2g2tLhCbI6J6BE7MwH/Q0mKFuPZrqaOsMdsAjE2z3m+s6CkNshTZyWO2Ob2W/Ow+rTzHduyg2P/I+2z/ygz9ERihITWqcbwobAME378zFsj/FCLsejULyfAee98MPP+Smm27q8D78N+CIkPI9QXViluTiZcg9XGQMyKA0mdXzUEgkKa/9vrY7DxPQ2hFgGiGTgo4RCdH3nJ9xpusy8+05VFdUNZXRlYtso4PaWLOfqz7/CdmRfuwp/ZSjc88hO9adSG0KwhUgBUIKhNQINCgS2i5uu/54Jvbq06oupRQVMYtHVu/luXg9T/XuwZm9s2lwXEY0CSkefnfWEDJ8ady94ZWmbRPVVE7rNRLbtbGcOJZjYUmL9fVFLLAWAIouaVUMDzl8nbKLYH03Bvq64EgHR7k4ysFVLg4uiVAtKrS8zfu1+r3PGPTVB5SlZtN97IgW+4rvOAktquhx5cV8csJvCPhaCjH7a3ewtWIpm0uX8Nimz9lQ33IGbMv/XJMSTunCiTffDzffD8DGrz+CH952ePYO5KG4CltBHR7HWpvQNUV15ip+cOG15A1tHmCUUuAqlKuauHWk7SJtiXQk0mpeWvOWkNgTof6CTygqL2XQvCuYqG3iVd9fmHXCl0ghsG3X4/hxXSzb9Xh/HAfbSfrG1EfZUVpBQc9eCN3AdRWuUjiuwpHeuithfqVGtFoysqeLK7xv3lWeKC10HYTCVooyR7CSnoxiGRINF4FARxM+NKGj6xpK60au3MzQ7O2EOklh3yiAGL72tYJ20tzj6yBMPStgQhyurGg/TcH7AZegqTOyUy1rH/FElDSnjs1ZY8n5wQz8ncgPpZKsuWYohOH3gZ92jbFmyE+DNhazYeMhDLZQXFhPV+DiLzdQ59/X5FCO8Pp4x2rgHD2H+qjiq3cf4Pjzbsdx4hj35NIXaPQEik8wmB19BFcLkKvtJ011wVVRPH9khW05uAkNZSZodHNq1OgqpdB1nbKyMurr64lEOkMq9/8ujggp3yMOqUlBoX9H9ahAdUrCaTL3tJMPxw+IDnLlANhRLzJG95sITWPghb9kzdw5LWZeprSZsO0tvn4rDWUEUEYAEcnhxj1eJtc90a0IXz69zxvPncOntzqHkpJNQ4YC4LvzxDbbIYTgrC/XszPitTc/7EcXglTTYNf9Z7ChvJ5pj81HOAo7oHH+2EmcP34tb6xZwD0rb6R374lcMOXSVvXe89WrsHsBXfVJ3H76tfTr0o0zP7yPaSN+yK2TL2yzLee+fhs76lfy4swtCAGmrmEYAlPXKNldQT6Cyd/OazETkq6L/W2MtJ4WxcZLfLIkj/Mm/aRFvbmpfVhR9DmPbfocUyhuGNByxuxI5/+4p7+0LU8L4GubS6dtHL4mpageulJK0Vcz0AwfQtcRunnAz0Bohrdu+BCajpZc13QDdBO7qhQdBz3QctAVQoAh8JR53nvZ3jDesMUmsQciA0YyaHiQ+MRC1AN9CAqLqXNOIfGLLUTSs9o5+vBw9FNfY0Zt3jyt/Qy+AJcvfIf9TpBrj3uzw3Kvz6wgZG9lyet/ATsGlTsINhRiT7iR1OwC8voOJxJpzpzb5BTbgYbDSoYg+zqI7jljdAFrq7pQF3eI2y6xhEM84ZKwHGJSkZCSEIroYZKpHYyaugq2vn0z44Dy4Zcx8P9j763j7aiu/v/3Hjl23TW5cXdPSHASCFqkQPFSCi20SGmBUqQUK+5aXIIFKBJCiBtxd72S6y5HR/bvjznXciWhfX58n/Zh5TU559yZ2TNzzszea6/1WZ9Pas8j7gPg+nYO+9MSKXzgHmQ4THJuDybc2jkYt9WOov9M0aEE8KrEaBoQxRFJaMBkX2oc+3J20LMyzJ0Vc5n50TImHlzPCbS//zyJJj6ZT2N4EHJoJldef0K746z6bivrZ1dyzrXDyOndUQoiEAjw2GOPsXPnTsaPPzIW8T/ZfnJSfiSzZCsgryuzJT8YONtsQnJUkZRIlHvE1UkkJRIyUBAM3FzLtt2fowoNW7Ojnb2C0FVyzx6NMJ3jKG3AeqZlo7WZzdX0Ogn9gEHvA9+g22Ea7TpKNQ2ROwrbLGPlRSuJd3fNYSAUhQ8un0Bj+ToGba1m124DLMlp0/u2A8fWSZvEEPy9fy7jMtpLsg9JjUWYEqkJXG2UhQekOjpHKd7Elr9ZZoTPF/yRJqOJ1TVOnv/j8x8nweNje/khgJaS5s5sR1kNqlfh7uV7O6x7YeU3KEi23nAv/e74Db5ch+hp/6zXMQMaCb3qkMQRrP8nHywsR1FiOHnc1STHOtUqr2x1VHn7x8Tx/v55vL1vJBNSchmQNJCqxj3E/EACwCOZZYSdFMQPYPz0WZucFNoPsNNYxBD2weIvftiObWwo0B+dgHLNv9xGC8AiOkHw+OKo/O1m9rx1FSMDqwg+NYK1I29n3Nk3dEsnfzQmOTocjqM7feTQVI/IXjyUMXTnEwTxkCSikbbljlp3g5nCO/V3c8jdAzXBS2yic/DQnt3sW7uDfuM7gufzm/wYsYnM2bEbU9EYn9e5Llhako+0bs5t9wqbwL+p/7N770omFHzFgfgBZA864cg7RK0kN4v8cDXufTsJayquQweZQDdOipSgmYT2z0HoPoTuA90HmgdUNVqsZpOW7uDsHp/Sj35p7XExb+1bxm1FcUy5oA+X9RpP7pylPF+1lg8yWx3cO9a5KK3IpGjwEDJEIe76lZRvuYr3HlnI8T8bRUavBDRdbYmadFXS7na7EUK0RMb+m+0nJ+VHMlGwnx1bqrlptRdd19A1Dbem4dY1PLqOR9eJqasjpiaBbUsO4fJq6B4Vt0/H5dFw+zQ8Pg3NpXZ64wroEvDa1lowKZ2Ecmvq6glbQcJ2wOmvbQnSckqIpE2cGs+heRvJmuREONrOXi1LorYB2o0/7xHgkZbPF742hCpNBbOE0T3O69ZBabY1ySM5lLOBT5cXgnSO9fuCOm65tnXmYAjIVFTO7te58Fy/oalsy3VzZ3UVdy6oZLBU2RcoIhF4fudtfFy9nIqKL9vt47FtBhsmvmg1TyDSLJ7XPrLQUFOGZUbQY+JId0uqbY1zc1P4w/nDMCwbw7CJGDb1+rnw8fPoCz7iQFYOw/7iDCDV779OstvClxah2DidHO8cYC/YsHHNS1SHMjFkAgPi0tkXPERRsImTMocgpc2Cst3MryggV7eZFPc/G0kxwyF0wO35/zeMPNhXi0k6gUu+wjYNbDOCtAxs00BaBtim89kyo58tpNVmnW3h2/0dKZXfofr+deyDMyDYTrolamkZ2aTc8jVbHjuVEaG1jN98F4cGjCN36JR/+TiNEZP6kHlURbk23eAe2li8BwxS8Px1E80oiMa6SiKBJqpKDlK3ZiUNVT3xaTZN1WFCxQZCSSawbyVfPLGRW2Z93K490zQIVlQQ6tEP1n7P7C2bGH/HbT/4WgEUaSH/jZJsgGDY4UpK/uWXJCZ2rcJ9uPnHT4D927jqw69YcdvN7CvY1269EQoTbmzC37AHxazHiC8nuz4M73RfXdecrtGUjs5BZbASiMNvNCFUlcvPfIOLjDBPvP0E72nvA/DQuAhQSFPiFG54/3skYAbmUXdgBp8/vgklRmPaxQMpLS0FdCLhcKfnUVJSgmVZ5OYevbDsf6r95KT8SHZsyX5EwA8eL9KywLII2xaGZRGwLRRgGuBr6sGSnV2XlVpKhJrUtQ5zJw7JlkDgdQm0SpXFf9pGrJGBT/OgNou1qa2vxaEIJwZ0Zn+6h6WLixwBN1VB1QWRYANm4TP0v+Qmzjqro2ZJ4W2LseoiVG/Jxw2o3taZthXVWAmU7MebnueUrLaxoKJwVeJojp16HyMSu0fmN9uFvRJ4vBwWXTKZ/ZUBHvx2F88crOCZ27/GCxyTEEPTpETKLUlt2CDJ3bH7P3dKHtsKywBHQiA/GMES6UQ8I9DDu1ocFFX4uLfXqZw55S+o90dnPlEnxR8VKvS1IYQr3reZ2jMvQo1Odl8BXj43kyce7ETU8W83ELr5cg5OmYj67pNs+vJtDDtEQqOf+AFBhALjNmxk67ArGTnmPLbun0swVIxP2UCmazfJkVzGp/XEJVy4LA0hNKamjwVFwW+soda2uOOfd6ApGrqi41Jd6IqOruotn12aC5fivKoiQFK8wOeOxaV5cas+XJoPt+bFrfnwB2rwAi730SsaB9QxqA27+SEJIiElmqYSn9Ox/PxoLWx4oPI7hPvfoAuXAHYHgIyiaYy4fT4H9+2i97sTqVv/yb/lpJz+5mqCZQFicrvWq2o5JSlQjiKSYmOjHAY/jktMg8Q0UrJ7c6jBZt0WuPJ3fUjo0wfTsiktn8zif7xBYeVOXnvKUdcWisMEfLCqFVcme/WD0uLDD3nUpkjr3y5B1gyHhj7e+8Mc5uYSaUU4WkptS7st02LSHZ9R7XZ+h1RvFRf1H8CUvgGGDvwbmCEndWYGwYogrOi9IQT5dXsxgovJdHcExvZJ6AdVNrmxrf2bS3dz+9V38N5b77fbNrbuA8DhS5JtiBFtv8mSf2yHqCurdAFe3r9/Py6Xi6ysjizj/232k5PyI1muS6PPoNGcdtppna4PGyYP3/8w8WleZlw2nHDQwgiZRMImRsjCCFvs3XSIYI2LIb1GI5FYloVt29iWjRII43UJivbEEFQkybGqI9pmScxIVLjNdjgQepoKWq1BpM5CSIkiHXJZxXTE7GKMzh+MiAyTWJ0E1WBLG3dSa2crEGw74GfbzTcyamAiJ933brt9/YrgjbqNNCy+mWGnvwXqUcwnbQNNqvQamk5vIRg1LJ1vFh1kU34N5WGT+fV+epfFkFVrcsvGNfStNFH7xpGeF4euKuiawi4rBCp8PrrfYaqlrQDa3AXrMIXK6ZOHUltRTCpQK2NpruvwRzo6KTUlB9AkFP3iWDhYRI/vD3Leiq6J+DzJ8SQ89yb1N1yJu74aN+BKNkgf3sjG+BMZ3bCQiO96BuUOYVCuE4L/bNF8qirvx+dT8SoBkI1YVgUCC4QNtoUbQVNNDgvMBVjCwsbGFs7yr9qE3Ta3Qpdii51ZdXKIwjEuxDf9AEd5OkqG3/q/FO0+91cb0NWETqsojtqala47GTTaWqSkkoZv1jkhR8UZlFEVhKZh5YdAxnVZqtq73yDKSSZs/DCixcOtrilCXIaPTy8/MoYgFC7DxMf7342IViiJKNBYoiCjVUE2ca4wViSGUV20U1/jMDQ1KwprqkKP7DiCLjdGcjqlUbB7W47dWF1j0sSJfFsfxG4zSNq2TVOTn9rqGhqqa0hISaZn747EhM2mSAv5L0hstDPDiWIq+g9zQpvvfkXAXpHErJxz+OJ3r2ELgSUUqr1p/EyvotfAUr4t0XluyzXMTy5g9vRziPF07UQe2vc57sIV5HVyq0hpACrrd/yJ+j21URFHFYTCbT0zKGw8hIlk+hqb0O5kZOF+YoM668Zf0yV5wexH7sNuKsNOzeSYCy+jqqqK0tJSysrKGDVqVLcVWP8t9pOT8iOZbdvd3lBuXUNIQVJaHL1Hdp7prQ9Vkr8yyAVXnd5lKPi5388hrqfCmbf+8Bnfwc17+PRBSE3sPBWT8Ycx+MtqIGKj+tykDmjV9Tjrtnup2buZz975gn0Hq+kM6qpImN20j18Uf8+AvjOOeD61hbtQfSYr5pxEVsIF9J7yay77+TAua7PNa9cvJGhDUBf4IhI21hHaWEeoeYNsHabFkdINh0efSAV7PNmoikrgpZNBgfy40W2cFIf4ra2TYoQCaEC/sy+lz8hprJt5Gd4j5IdD/TOYO7wPHsNg8NBsjtM+QwgIpwyFhoUdyjUNK5adO47nyt/8jl4ZKZ22+fjjj2OaJmtuax+Sl1ISsSJEzAhhM0zICBE2woTNMGs2XIiiDWPgkEswrKBT2WQFiVghIlYYvXoZsJdY75Fn/M1WG5eIoVaQqw9FShuirLKSKNNsy3u7Jb1iqZXYHte/56SY0XC4q2snxTYtql9fgtWU0cUWOdih6naRlIC/AcMw0XUd3eXGRsHuEnrbva0preOiV1djB0x69k+iZ/yRr7jGUvCqNriyW5wSx5VwZApsVCyhsrx6F96IjzO6aKe02Lmmw2/N2OQMKCllUL8pnH/FzE73nfPeR8hon/XZnfcyaPaHLes0oCAljZylC1tYipttw5KXSV37Ir2bDpJ5oBp49ojX25Vtra9nIiraD8QCNTtdKoJ9Kf0oq41nlLsRVTrO3VhRxY1XnUx27xx+LyVvL/2We77JY+nO9Zw2+vgu2zWkdCYYnfS/idHbI101QUaiek42AhshJb28Opq08Z8YghOrSP6TjidicPyK25l9TBxfTNK4cOd47LQkPPv6IGUjyDowDZSyIhbNn09yWhpZWVmMHTuWkSP/3bqp/wz7yUn5kawzZdK2JqXEFhbuLsT0AIywM4PuNldtK6105j/QjJDT4bu6OIe4jDTiMjp3oBIHTiRx4ER45wvyenTklth6xVbWr3+FK7c9i+Y6usHP7B1BrwBdxJMffpyqb+YR5xpOet4MVM2HqnuRUjJqeCpTf+s8sNKWWKZN2LQImTavrS4EAqR0U06ZICNcGNmNoo7ia47hN3zO6KZl7HxgCkE9iZ2pvTl+u431+uWsr5P4wrJlYHV5ow6dZbakhzqzHfv289XTW1AVQcDtYkd+GWpCHtPSCxh74AUaRAwJWe3LrJsJwrqrsnAI0TrBKAmBW3Pj1tzE0f77Ltxq4/XmMbFn50Nbwfo6AuxFPUIKxf/PN6h85hlC5UGCZ1gIj6Dfz//Z7T5trXFvHyz933JRwAghpY7oBo9V/KePEa4c7HAN2feeDKaJtGykYSINk/qXH6Rm0VLgHADKivaR/o9x+NrkCLKAAvXoI0sNIZNT31pFIGwSCpnYAZO+/ZO59YR+R7V/QUTF0rNYm3QLT46e3uV2V7wxhpxu8F05eYLdOyF/4UriMs9Ei6rojp08kj2zN7Ht4BpOqZlGQnLHZ9IyDYimbTMXLcDQdSpvuxNvchL6K4+ScaAA9W8phBQXYcWFKTTcdoQxVqt6sTfSQOCxLKSi0CgVqpUs0GKQQkEqKgg1qg2ltnxGUbBROHSwlmkxlRiKypg5G4hW57ZIiqk4A9jwzYvoIYK4fT5UtxvN5abo4H6yidYSuD14pcFz913a4RrBeVamjxjJPd9soLimptvfZVFNI2cCa7+djxDx2ELBFmBG/MxJtEEZwOQRTzA6bXCn+0cC9Sxb5dD5a5PzcI3/NZEH/8J5KxpJ0p5ECp3qhq2knfwGPTIKKV2XSvn6NFLz+nD5XXf9n9LsabafnJQfyboaTJotFIiAsHF7us7qFx+sRBLDZ6s+czAGugu35nbeay7cLjdYantK8x9gkVCUW6Cbc+jObNPZf/v+Bk6oKcWd3D5furdkdbT9hA77dno+ngiaEEyc+TnFGz5nf/nfaVK3UHqoNVVjqE8TcR2AKBuDUASaS0VzqcQARW7AgkDEojNll2A4yFpvL9KDuwH4zX1vsX/rKqoXPM2EujlgwJb8bfz8ax81sRBIcFPvUsk6FKBwYCLH9xjgHNcykW1SDlJKln6/gdUbtmLWCbylaYTcfr46uYZ5579HYlJvjHuTKSKDHn/dgw7EH3ZuzUJvbSuTDrcj3Vdd7NXtPrKpDoCqG3/uEPtZklBhOTJioacn4pt8DE2LFtK4vRZvtkba2RMIJS7FcP3A7uR/gChFmiEkerfFo9L2gXGI5J+PQEvoOKCrXhNht8TeiDTVonTCTx+b3VGvpStbVVxLycF6YtO9xMW4yEqN4avLJuDt5rdsa5NTMlnqT2RWnZebmmrIi+1clyiCjasLRXOAnBG9UOYeYOW6NFauW8Xw3kU0NiqkZ9rENeXRGFvA7s17mHBCRxkMraEBV10dXzzxLP1rqiicOIUZl14IQOmcF/EXSVYd9zB2JIBiBBHSwlB0kJA5+GTyZs0gHBuHkdEXbJNtjRpVMp1ebhWkhZBOylJIG2GbCMuIvrcQ0iKJMJXhWFann4uwQReAENgCDCEJAZa0GbRlEY3xSTTpblQjgmoapJgRGlIzUYRwnpEjfN+ZiU6U7f7v3JwyvIC89M7TWKOiMhQZ9/8Vtal9q9cD8158ldzYXl0ep3rvcgBERKAsKSby3V9aVwoXltaIdNcxwOVGK5tAqWriO+lcLrv65/8nHRT4yUn50exIUvFVFdUAuD1ddziGX6BLlc1zN3e6Xlg6qXIyYTpHhB/Jyg846pxfPP4X3O4kPFYAj+pEgFRVRVVUFFVFVTUihoKd1R+ha6iqjqJq7ap7Drz0F/oMm+hgT6LLAzWOeu3e/AJ2FVQT9Lnx6m7cmo5Xd+PVXHg0HY/uJkZ34Y80okcfzJwx55DDOTRV5GMEqrGMIJYVZrdloqU4zlEoHGbNli0M6d+X1ESnU9+lmGDBuM17cBuSi4WPh08ZiGmbGLbB/h3zgDzmeloF1/oOn0Tf4ZMwjQjrX7uRE/J3EqCcce9+hHvQ8Jbt2s2VLKsFZ7Nxz3a+e3crMRXpKJ5E9LgI9X328GXi+9ydN43EJCdNZqGyv+cFdEVsbTVHUrqjJz9CGrEzE0J2S2Hv9dViJxo0bcrHthyohhbvQk+KI7CnjPr1s9HjBNm/PZP46x9CqCrV351G0NzXZZudma+unoj975X0CjOCFN2XSguhoqVoxE4e2vkGltUOjtJjwGh2j7mLSEMVViSINIIMKPkcq3xX5/t3YjVBZzB75NwRzOzVkWNl8d4D1JkWaDp9kxMZnpLYbv2Hk87kwe1LeKbC260fZ0gbvRvcR3yfflz7TB5rXnifkmLB1oPO3XaoOoRXDdAYC3GyrNN904vycdfVkrv6U+rj4lFntKZoFcNAuFxMOuE3ne4LEFEERq/xJJ3jUPLPXvMp6wM+1hzfNTvu9o2bqa+qwaVr3PPRDkYMSOG3A7M4XjdRXC5UjxvV48YTG0NyTgb+QIiX3oK+x57DZZd1ZHuGZgaf7vtfIRQGpVSwqzqd9MTOKwUBpsfo7AxCwt//jm/wBBQpqa9cRfCiOwHYc8LUbo8jo+rRk8YuxF4Qz6z7H8YTCJBdUcWJvgCFngBrpUrPtU572UDs1eO7BND+X7CfnJQf0Y4YSQGSk7tWcg3FhImk+bn06ktb8AURI4o7MMIcyq8iMBcyhh5dpOJwyxnQl01zITa5P8F6CytioYpahzlT2lHKcRvLtrEtC39tHVJVIErVHeXMRRcR0hu/xr3hk3btJ/fMoUZVuXHH0Zc05ug2Cxb2haRHOWn0udy48lL2+RvQhYIuVc6wH2Xrt4ms3PM2osFFbHU6W9lEGoW43W7+7lmDFptPhVeyPyabRreXOV+kMCeg8mXa8YQVZ8Z0QfECtn2zGVVrdrp03BEvIwZfQuOuz4hklxEpDeEa0FGhFMCORAgFI7z36ddUzdfArZN2XohzTzwDTdXYuuNjZq8NMqjn8S37KNJG83Qdqm+OpLi7iVBIKbtNI3ZmimKhiK6jZXq2Tt7MKtS76zoeLxLCKstH6zGwXRREShvxA7V7VMvCW1v1g/Y53A4dqsTncnHGnA24Jbht8MjmReCR4B6VylV15XRVByEtux0/v1A1Bp51a7ttSu5ZwK6iSma9tSAKylZbwNkuVXVeNbVl+a7UKZ3dWFvLsTmJxLZJN24ureCiQw2tjRfVcmucwnkD+pAb64tCix38A4C7m0hJBNmtkwKguHQm3XRFh7+X71jJix9tY+e25Qw+8fQO613+Jiyvl6zXXsauq8f0N7HvvXcw6utQ1+xDc5scXPQOui8B3aWjNhrESxNphMEWuCMW9W0iVIYNhTKTSUu+4/e5Hsam9mdgQmtZsW1Z8IuLSYpe93MACx023sMhy/XA9pvuZOhFDhFkd4rNDufMkc2SFn2TqvF2w2hrR5lsEzMzict27ihXwigKo+u3XjAOtVdG67MgBbETptDjwjucj83OklDQmwSneU93+PejkLOhdgIDvG5kWKKLQgx7OzrDgKNPNf632U9Oyo9kXUVSynfuxPT7yd/j5EIbyoL4q4PYMoxlSw6s38HIUyahqArCFqgehX5Znee155WsZS+NEPzXCJQGHTOZQcd8BcCbV39MdrbB9Ec6MrJWL1tLxTWX43niQXrP7FipsP3NbQR3VWHdPBaBCaaBNCJ8YUWok5JwMMTZ767ilF4JTB2XScQyCJsRQtHXiGUQsQys8DZ6qHuAzdQ2FiKlzdaGOnp6NAYlDcAwLSQ2AoW4g618AQWJ22lsUujVpDIm6XXHd2qCk5ugUY0lrrCJmQC2ySfpM8moLsFTeYjZuxuQbXAliXYMZ0TG4vGcg3cC1H8bwmzYQNLZHUPjpgmp+asxnhEUTj2Z3/35HGJ9rQ7Iot2Ow+bSnQojf2MdMcJE09s7C1V1jTz24uvYto3PcKqt9G7wFqFQiFAoxDvPfIXPHYuqKaiagqYr0fcqqq6gaQqa7rwP1/XA0vZQtG8jmu5C1TV03YWq66iajqgtQbWlg7M5DA8jXB60noM6ORP7B9Pz+90uYsIRIncnI5vnulJE3wsENhHciMs/J75/5xUx1VkaSoWLM70xhKRNUEpC0SUoJTVIVie6GeRx0blkG0jTQhyF/HBdIMJnu5qwpWiGQ9JdIkGqgmdrmihcvpanp47HE3VUntu5H4SXBxI1esb6+Pu+Yh4TcTy2YT89Q02U6h4MVSPZ8oAK7m7KeE0kuvKvsbom5PQD5rG7CxiGtCwq0tJ47bN/8rNPP0Np04dZCGIzDLKX3NDtMYJlYFkRFEXjqry+HNhbRKXl5ZbCOJSCYrZP8ZIUTf+Gm5pQpOTAVdfQ/4yZVFbW4zZs0HUsw0RGItiRCDIUJvmvt5L61AN8u2QF0L0YoqOb1v3vGwgH2VuTxVXj67vdTjZXk7VhY46J7UPul+9Q+LtfoxQFkdWHsKO3haiNUL+tsMVJaVZvLt4+m559f9npMVx1XiQGGa4bWFTem7LHIlxw1wPoR6hg+2+1n5yUH9HaRlKCJSXMe+01Nh4Gu980v5S1H9zd7m+L3gRQ0BEYcliX7cfqJVyZdiPqsgih5U6RYssiVEDBRkOTbsqtGwmJvkhFIBVH96flvSroFZuMolR3ehwrOptQ3J3fPo5qvYoSF9su6pAQXayAQSicz5iE3vx8eEfGy8Nt3vz+KIqKaYUISTi19+n8avwDzsrToKa+no8//w7LtPmu8R/sSyplREMit/9uGdzbvhMNMgCvupF9Ri5fbRmFhxJOKfmUIcJFUkIyRrwTyRLAeLOjM2g3BTr8DaDgqrPxPP8MEWUTd9z/Rof1DRGHqTKv5zQammrZvfJjxgN6bCa1FdW4vR7cXg/Pv/E+nrBTFhr0ZeCNTTiqSEnxoUPENvWJEu8pR3AYnFDy/rm1Xay/AwWTvvf8g+n3X3fEYwMc0g6ABmbtXrSko8NurM9IJF0EiLHORGKDbbdiFaSNUl9ATmgzZa9fgD8mFSlU5z5uA7LMsssJqzr3ntA510pVyGDY99uxF37D/O3bwOMGtxfhdiEUBUVTidtdTuwRvmOPsDg91+AXZw7Ck5SF7ovHtm0MwyRsmIQiBuGI8z5smDREDApMk1vL6/knXuYu3sgvvbCwPsBuXwLjww1cPnwKuqZxSv8+LD9QwJ93FbDH24pMqokCdR946j4UJIk5idxx6R3tzssEtG6cGKu2igU7VyJ9McwYdRLhxmqee+LvBKQbgURB4BadV6Wd9YtLWLx4IXt1Hd/fH8SXlIQrLh53cgqvvf0GYTPMDb+6mLC/nvzNe1m+YwMXnnElST1yQNiUPb0EpTCH0jsdLFoeMPvqXMyeAxm+cjfD1HwSXKNbjheJOCkyd78+DBg6iAHd/B4rt28h6ZO36bFzLSUDM9G7YbZdVOPFryjc8/I/URWFE0f3Zeq49v2OR3ejCZPEKPeTYYTYuOk1LCuCEKqj6EyEUPU28IF6mMMQ138cQ+du6HDsnTfMxDxY0vJZczXTIAjch/HlvNDPxet93Ty4rY4zgo/jRyF16CD27NvIN++dT9+Jg9H0OHr3+i0u1/+MTMN/gv3kpPxI1jaSYkci/OOpp6j2+RinqAw8ZgoNa7dRsXAlPS44lW3bxlFctg6A2MQ8eo05FsOMsG3lx5SKrV0eIyHuEDFqLQUZl6BqyUjbgujS/F6xQuQ2fIRHK6RJG4iwpbMYNop0dCgUW9LXoxN0d54SsCJRKfdOyNMAMG0suqZ0DoZMJODrQuSw3bFsyXOVOgWRl7ln/yuAIMHdPp2VnJDAtVecT+OOrby0pgQQZNlOB/Bq+bvYai3Tz00jY/F1pLOBIjuVUyN/b9k/pLg5MTPKNHkEKozAus2YFQdQdA00DaHrCF1jemIu+TFuTCXCioMfkRXXlz6prREXf1MpY8wQqz6+mSm73mY8YEtB+b2v4au/v2U7edGFLe83Z6SwN6MH7367yuGzgWgqIEqZLUGZOB3TsphZVc/tV7ZWgdi2xIxEeXYiBkbExIiYmIZFOFSPrhlYpvPZMg0sw8Q0TWxTIhuqKVpUTp0/8Yi/T7Ol6AOoNvYQqt5M7FE6KbVuN8EkwYyZL3W63g4HKH5qJopSD7aBIsOIqBMjcF7rdBeLEybQVc3M5/scvEVqRSnxK5fhsSLodsdBuTQ5me4o5QzhIrfsW3j1WxpEPPo9RSiKgtvtwu12dQA9A0wG+iaVcuu6HeyOT+FFA/Al0DPUxKfTj0FvgzOY2ieP+4Tgg4PF+FSFySmJzC0pxd94iPj0GIwCg/LCMn7/3nFOqhMFXajUCdDaAGqCS+dQ/+6LRIrLMKr8LM8exm2//zOE4flNS8htKqNaiWdqukFyQhy2bZOeO6LjyQM5x04jobgQdu+lx8zT0dpEGS0EUuh4cofjAZQCN5UUoOf1QU1zqvu8v+hHqLwKaUnkAud5fHBZhJV9lwCZnJWitXPAQ1EnRT8KDqUp99/B5/UB8hZ9BoDWjSTIgCSNjVU2s/bbGELh473bedvrZvTQVhyaoijEu/2UNRj4/TVs2PAyEeMfHRvzgWG4sLUj87aE64oxhR8r2aa2di2KouNv3A9A5qDTiUTqSbpzGKG9fuoDqxhV9x43Si95Q1fj9PJJwBr6R6FUjY3gD+xEVTz06/enIx7/v8V+clJ+RAs1NLD9q69ZsXQJ1T4fM4cPZ8J5DtirujyEr/Aleh+fzbBr7qVwyQa+evkRgvXFZMXHEKg10E2b3pkd1YCbTdoOn0fs+b8jpYsSOLuuBp76iOQJPcia2VUAHIr+8C3xMZ2P2HZUSVntSknVtLskJwLwh5z9fUdRhWRYNvlhneHxvTkxpx8+3cvMgb/qdNv9e9Yio9GqPklOB5Q8oJY/lKfw0DwbnWc4RVnHEmt0uyS1FAqNIkiZqKPWXUuC9DEw1KvTY9iRDGpeuZeoXGk78wJpcX341dK/tfztyzPfp1fycILhenxCMGXX2wB8MOB23tyayD3h19k2YgrJ047DDocZEmoiohpkenTmeONJC4cYn+DDlk5u3UZiS6diwcZh1pzriaNBtse2KIrA5dFxeXQ6is4fmaWyYdtr1Dccvaxx75xfUp1/O/wAGvSEhPGErNVdrlfcPnJuW9xtG9d/9g0HhE7ndwSEopcw5OV/MC493nHeTBMzGMKybEzT5HdPvEdmAhzfzXFcV3xCyaFtsOJpMqOVYEdj43OymLK/kN2mJDXUxDtjBjI6s3Ng5vG9e3J871YBvQuGtbpNz//9JmREodYI0CRtem1rJL5OcEstDKlbRfnccwgdOESgwI/mk3h6JBA7qhdGjpOamxHYxfUMAhJQpvTnzHTBpOGjDz+FDmZFHbrDI3O2baO2cQyMcFSU0Nc6sUkeOh6Ggj9i0lfbxvhqE5cl2W87OJQvggOYu34PqhCoQmD5/Yw78TQmHmUViystteV5T6yt7HK7z25tJdC89MEPWd4Qy8/e2cXb54U4dnwrmHpweoB1hTpLlv4St9txE0aNmkd8XAagYduwetZbLC8o5vgJjlvaeHAj1Tvn4I7PIn38RS1l+0ULn2WP9RT8zGm7euNF7c5pzdaZVFT2YPduB2g77dh3SPFBuT2KrWI0WZEUxuaeQnn5PLzunuT2OoOkzDyWLZ+M2901sPe/0X5yUn4kM02T1Rs3shqIVRQuGD+eoae3gtVklKNE8TphxJ7HjWGmdQuzX76P7z5vnWl6ikqoDZST5OtITGVFKaR1d9cU0jLsODLoR8hvqi6E3nln0RxJOVx5tuUYluzWSQmGnA7NexQlq2HTRAqbfolnc/XEqzqeixlB1VxI22bE2VcS9+rTNLptqv0OILM8ORvKne/WQGOOPYlRVXsZUn2Q/Qk5rM4aipbTnw+1lQCYmCzLWI3fXcvo4AjuLroS9+hkYiZkUvvyTtS4THp9thg9y4uMGMiwgYwY2JEIxR/PJc7l4rFJNi9ufoX9wRBnfhnF9MT4yHH1Z+O4X5DZYxQXZfbjImD9N6/inTyKk353eYdru/ejuYxvquPJs7sXVus3ZwONB0O89MpGVLUVj6Lr7d/ruorLpeLSFZKTPAwZ0DlBHIBhgKYdvXhZTdgBgq4uuBUz/1asKG7DiqYb7WjKMTvvJib3c3Lx0jaBf6+6JyLB1Q27bjgqHuiJ4noUReBy6bjaONhNbh+qu/sQWlKvEST1GsHBg6sI7S/gh/CfPnTsRB76Adt3ZvFSkBqjc+GVa2l86xEOfe2kFD1ZLqQlqV22E0+Gm5xbziPu8tsRzbpLn34NwIvHnswTaxZR60rnPTMBvy/xqI5rRFO7FaUlZPVodaBsaaO1AeyGQg5A1u3t2K/sq3P6pbUpGukRiUs4scAd/nAUhxQlX5M2qy+4nJxQV2nI9jbz7hspPHYkvPgYmfFHR5vwwu/O4J8L13PX941s2V3Qzknpn+ZifXEsQttFKNSTMaOfJiW5b7v9dy1fh8zNoX73Atbtu5dwesDBtEZg17cP4vNn4VEzqPZtwlefQm7OZZipEhHnxrbCBMoPYW1WSSieygZlEwiDwSl9aGrMJjauhAH2dkaOmI2iuGhqKmbctCdwR/vz4uIPkNIiI6Mr6r7/TvvJSfmRLBh0nIOJus6MO+5AOSyVIqMcJUqbmUivEyfwq36vUnugmNQBvfng6XNJ6F3MshXHUKbmcc74Vzl/ayV7wm68dh1nlNs8C+h6N05KVCxP6F0/1NKSCEVD6J0PIHY0LKt15WRYdgtwrDMLRCMp3qOIpNQEa5m2zYY1zzL3e2fWrSUmcdyv7+WDB05ic00ta/sLauIFp6vZHJt3PV+XPcsnnq1M/3ouJbsP0KyRATBMFPOn7Qsx9Fjq/RVUjfmEAbuuJraxkYP94qhz1VHjc1IEK+O2cea0GD5aWUtko9Nxil5uvIM6J7RTkhLw2DBj4C+YMfAa7l14CbOLtgDg9g3mt5PvYnTu8Hb7aKaB6ur8tzCFclSq2KYKmgXBjTUoErSjBLAGfjOEcSM7F20zTYGmHX0kpc7y8nqVi+npo/D6krDsMLYdib4aSDtCprmLgxULW50UTJD/ppMC6N2U90es5gqZrr8TUwq0ozwNYYaxxI/fbYpQDYdCqbx+541Y5X4GZqYxbcEiRxRRSqRlIjohE4xIiWJZ+OJS+ctJF/DuyjW8Z0JTTV2rWl43Fp+QABzCF9u+T7FtidKmfwgHQuioneKnRqbHM3StZLtPsPy4YcR3Mbk5dKiUcXvLSe1i/dyzJ5NcVE9TohthS/SwhTsgYFBPgt3QLpR8v43qxfuimEDByKjsh221/x3PHDOCt9YXsahoKjPyJFlZHdNgmiLQzTDb6/6ErnvJ40LS0k8jWH+QgwefJhxfhWkWcWN1HBBk07G/RVVUwg3VVK9fgz5vOEI6xx3cM4l1FRUkeOMZOuYJtmy9AY+niu07zmo5XnHxZUybdi8AqhYD2Jhm00+YlJ/sf9Yi0dkIQL9Jkzo4KADhfeUAlD02x3n4VYHQBEJV8KiCmqV76XtiNbFMZY+7mF7GDr5dfRGNyt3MYBNLxYn0C5VRqqoUbv+Q8WOuQekEUCf9DnpddkOoZkcdJtEFMDbsj67vgsVVWBK7m8G1uM5x2PzWkfVl/JVl/O5Lm5AexFKXEhON3+979nPGAeOASVUmf53h4WurBMocCu7e5RD/95u5BPgF8Nal5xOKxJAVqmPjqJta2j9vQynfDJ3DOYVTSSsbSN+UQpawBFtJIBRzDNU+lXd6lfELVwJxO700urpB/1sC2gzs9574HmeVLOHszYf4e79szjrMQbEsC5dpIrrA/hiKQhfBrHYWUgXeMcncfLMT2pdSYpg24bBFKGISDllEIjZhwyIcNtmxrYraxWUYRteDu2kq+NxHr/9jWgZbghoP9rqWvF7Hd7rNZ98NwAoV8f6W50BoxFCCakle+PgjRy5TCPr26cPJY8Yc9XENQO+mcsOwnWvwdFMhZUgFvRsnpp1Zkf8nTkoydeyjN406iOwEUrKyWlWbhejUQQGI2BKX1RolipU+wGTtohVMz+uDL70zNE2rhUPOsxoT2x7kadG+ECAUCuESXWNJLoiLY7vVxOwNRVw1pfOUtYymlkpmvcmWG64h6BGEPSqGRyOxKkxe2InQVkzq4eDBPB7KA3VQCQ1ZHa/j4NeraNpRjigxidOTCIhGXLgJSRMUeGKnwZO3O5EmCUyJ8ZARE2JFyUSm997b+TmmeohJrcZTEsu4n83DleREtBOYRuYxTjRUfv5biCwDYEPFBkb4+lL8zje4ivIQQKRXEcknD+eMfr9lx70PAZIePcaTlbWMgwe/o77hALrmoar6YRoad7Jy3jOkZ2Vj206/m3/gTYYMu7fL7/q/zX5yUn4Ec7lc9N27j7TKSvrfe2+n29iBCqriBAUVq+hXqaNnjcaKSQdFRwinA5eKhSxJ5JIrXmLB9kfJLn+Jx7gRgAhuakNrmd4zB7Y/D9uf5zziSfWloQmNlJhMThp3PfEBp95Q+LrmYzHrnUqU2nffpvaNhxCaCqqG0JwlXFqNDix9eiGx8RpbU+Mo7JOD2+tFE4I4n4nHo7Fq2X40ReBSnIiArgh0BRbvrADg04NVlMeAr7yABN0hg9M0DU3X0DUNTVNpKm0kGQj++RGmXHwmkWCA7645kz7rSth/wQQGzV3DsWkjWf+zF1Ce6M91xu/Z2NQXGmvZ/Ic0UjwqMRnpTNd+Tb1MQWtKp3xJJpGAM0vSswzOKKjh0LBVnL7uSVK0x6jcfynz+/RiQ5KDCTi7+FF8ZoQEbQRNezrXOXF+RAFK+4E9OaYvhicBn+7vsHkomuJTu5AhMBWlhcyuK7NsG1SBt00oQAiBS1dx6SpxdGy7ti5ELeD1dv34G5aKrh+9k2JYTgfqUruO0B2M6PT3VKBWvYCGiRprUXcwDv937wCgSMmKhBROfuWtoz5uBEHXTDNOJAHA2w2PhiEVXOrROSnCCmP/P3BSTrv7M06NDuKP//lOioIhFj33HCfc0H0JcERKdLPVSUlKSIXKMsJ2mCdeeJqThkzFleQlUN3IqJPGE5eW2H7/cASkjXZY9UxYqITDBq88/gi/uvlWTGzUblJ3V07M4/nF23gpVM+VXbAkR0pLAZ03jj+HJlNnaM8IdmMTlFdRlmEjDJOc637HGSf9vGWfxYs/pfStpYQXHWJv0ZIoslwgIuDZLIixfYRdQfw9ggy94eyW/V5ecICSyiawHaf+vq2HWOkPAT7Ax6LCCB114CGY3QCmi1Gnzm5xUA43ccZTLH0wg6/GXcio9FEU3zcXVyiPSGI5PW6cge6dBji4HtpwC2mai/79WyEAc755FY9nHUHWUdAGcrN/62f0yL6BuOT/G9GUn5yUH8nGb9+Od9Qo6r76CtXnY1HZCh6t/ZQmr8KwWp2do/2YExTee3QehoSC8iV8OWkiNVk/IyMguDTLQKoRRJS98qShf2RB+UuYEt6pduHdXcVX2S4gwpXxQ3izYQezaSCzqRETSV3TLu6bs4QLQwpnuV0Miumor9NsdkN0QLUjoCqOxkk4DJaNtCxUFGqSBlHZ5KGySfDa2DRqfBLNCmArYA90YyoCzMZO2xfp4NoL32wr4+D3SzilamGX5+ING5wA4HfSLS6vj9PfXQA4jK97vxqEcLsIhULEAz+fPIiG0GDWrCvljjOOYWJWIhHDz7Jl4GloYOdHffEkOA6KO6mA7AmvU+CKI97bxKUDX6In+Zy4ZTBJdmvEZAx7UDQJHCSGqex8dqOjFaMKhKa0RLy85TkEe7RK21cGa/j00A4gHVcnCrvheucY6vvv0XDmTOIT20e3QroL1eh+8Gw0nIHL102k4HALBp1BKyam65mvaWlo+tGr/prRWZ6mds3Z8UZtImN6nMdbx90CRNk3TxCIq51rfPj555Cb1x71MQEMIY4QSYliUrrJ50Skgn4UTkrBl4/ird6K/QPAwf9TJhQFEU2lxAlBeVIiy8rLOXj7HVQh6e3xcsG993TYr1KoNLbh6zGjkaULTzuHJf/8mG93LGlZt3TnKq675jpScp105uvPzeJQyQFQFV687maEUOg1cgKnXnchOanJFFfVUNIYwDQMiJYzd2Uet87lqpfHXSGu+247L0/vSKWQN3wol7z6Ll/17stbp47hKvVb4vQYTLUX2Z5UPK5Y6rUi1qx7EUV1oWseKncdZFrGeQ672/qOxw3mhhn6+7M6/H3GSe2jOb8IDOHzD1fx/N4ChGJz3NDjO70OXU/GssL4srvRYNJcJN1d2yKEqoUSifQ4RMbPp6FHRTt3bK9g5jtrgeFc3mDSGQfvsdO+5qO3nqKyKUCyKpl57vlYpsnc2S8zv+gFzvnj/w0tn5+clB/BZCSCDIUIrFpFYNUqAPoDl0x9DEvz8uXg5xlTNoSdqZ+hRPvb7LomLlixlqcuHsE70ybwz5DJGd/MoEfDQtbPWkByuJEZ/etYFeNic6KPK8un0jeYxwcDX+TNhh30snPR0dDR0IRGEn72UsSHHpsPszN5Y9Z6clzlCE1gaQolEYPaRBeKpuAORugNJN10I2nndQ3aPCb6+tynC7iisokHLm6dqUgpsaQzkzMsG8O2MUw7+l5SM7YfTRIWzanAXKgy/c+PYpgmpmFiWhamYWCaFuau7bDrcbLiOx/8bAsUt4dw0HGs0uLjmZSezJp1pfz9+4NcPz6Pr3Ztopc6HG8FKGorMt6Xtgd3Qgmja21+6XmZOiWZgIzhrrzLcCcMZwHDiTObUNoMgjtietC7uAklShClQMuCENTUt1bSDF9VCDjH83RSVqkEHUBh1sH9LL3mOs74eBZSSopufA/hSqf+9HReiIllztxNnKS5Ob9/BsNzEtHaDLj1URCz7wfQZodCjmPz5ce70T0qiiIQikAIoq8C1YhBVq9n1YLnowOkhlB0hKohVA1Pci/UcAMmCprLS1WFQ4mvduOkSGmjtklBisMcN2lb8APZcw2ULnk+wEl3ALi6xaSoR3RSpG3Tc/39RHBRljKZ/5dz2F8/9BAbZs1i4ZYtWAgUATXBzvl73knNAWDAV8uJjYQpSXLA0ulpSdx42y289+Rr5GX3JD41kfmrF/P2G29y1a9+SWJWCkWV+9BUH1pVPqqWQmPldrYv2k19RTmnnH0q27avZd2evXz+/jz2lu0m5gisqH84cQBffLOZf/pMjl9TwMUT2uvjaD4fj9/4azbOeZ9Sn2B+xW6KjnBbD7f7M5nj4Odu0noPcZSHpXT4nhBkd6Hofrh5fDoDM3VOKtjHNRdcSc7QXp1up6iebonj2lr5e6sxtkaYm7iCg5Fi7ow9E4DKSj+3fdQqbfJRJXz1l8/a7SuQ3Hv6IH5+2Z947PEnCHl0cvo4lUDHXarx1VMPU7p3F9kDOq/i/G+yn5yUH8GEy4VwufCOG0vC2WdjBwKs/mQ3luYMaGfuvJ7i+D34c9vvl9zYwCWHtqBk5ZG8cR45hYXYcT5S4gIU1iby4v5RFMfYnKDW4DFjGO4fTGzZeTTmGFiqRURGMKWJgUlEJpJOBitw+FeSwglYQSfdIG1JlgRfVRgN56ZoBA5Khc4hou0tomodgIlCCDQRBXF2Msvvm+R0Hls8Gn5NY/TwzqmbynWDGhxHpDOTlvP9hqJOisvtpbjeyaNvWFPC1WtKyBinMivlXs41VuEbsJtsdQ4j07bRM3yQ0qo4Hj5wBT29C5ADJvCUdi+RBIshbOUP8iFm7NvCjqQR7J/6DDfWBnk5tx8DBnUsAZRS8uYdn5CU1DpYXJZq8E6V45xonSgZJ/TqhZwzh70XXoTpce6F0N5iFF8eyCZOqW5CxMYRbym85g7z2oFCOFDIqv696ZXrRF0ao+XgMUeL/AR69IhjjwZqfhOWbOZecXhXiL66cZHJLgYse/Wo2iz0eSEjjZDa3aBgIbpJCWQeOMjwletZP3q0M8hoGvi8SJ8PdB2p6ewXCawYcSyWx4uuKpTEZVAsBJf/c3OUol7BpUcp6lXB0sJqRJzKrA8dXapWf1Mio1UluaE4dLtr4KW0berzN5MIlI26mbxz/tztdxHwNxEKBEhMTvn/RXNFURTGXXIJ4y65BIA37riDIl3nmdtu49q//AV3XCt+5J/9Mnht2176BuupNy1q9zfg1nJI7RlAdydx5e2tZH3pWRm8/fksnn3peQYmD0JiMXTQMZxziSOCV7B1L/Nff4tDO5ZwaMciXOn9IMHNzvxNuIwIvWtN6r9Z0SIcKaKDubQtR9sKeKxHD36/fzV/9QuC+4rRhEAVDibIrbqI1XzEIhmrJPDuL1s5oQwjRChYQzBcTyjShGWFMIwgrN8NhZCZkoSW1HXBwNFYM69TYmbXqXDHugFqN9Vx6IOvKQ42MF+sZVPvUg54DgHwxUeT+eSYWTz2cRVbw07kcXCMQk6y00c03481hmBzuckXWwtoaCgmpLtaNH8A+k+cTFJWDgvfeIWL//YIajfq6/8N9pOT8iOZmpSEb/QYEs92og313zl8GfqoauoCQbKzYjEigmtvUDntUJiZG1WS7RC9knYxITAK89B+GgaP4pJrfkOOUcV3f7+F/TVeejUBxEASaEIw1X8yYy/tXuTqcFv4wXb6bapm8INTURSFnRt2cdpH+3m7m5RQWzM0tduZandmm6bDHtqFWeGucRvSNJGWQPF5CYcc58Dl8VFX1gpUvmRGP/potdxpOiDAzKCLWrfJSSXfA9C/Gn4T7I3iz+L2PnfQtCOXxNEFeBuGMCZ+HU09LbTYy/HF5hBoKGmH/WhrQggsW9B2kvXo8PGcVbqZC3ZJNlSXcGzWqPb7qCpJfXpT0bsfMjqYVb6wHKSbjJsm8E7vnJZt/1jZxGMbC/hMjXDCrgOkb4VjpM6EnEQAfEeprgswflQm45/rvKqn2Xr9eQ7rJz/Ia2eOwLYtTMvAtCwsy8AyQlSV7QZfMi5FIRIJsuLAfCh+D8vXtVsrpO2wd3ZhsYobDYE9ZhTCtLBCQeyGBpSmJjAtdH+QUaEQH8UNpyDVi4VErfeDhKX5jbSQyRxmblXQVN31LP8EYkjugkkYoPCLh8nb5JD/KZ64LrcDaKit5YknnwBFJc3j4vrbu3doFr07h3X7NqMKFVUoqEJFUxwxT02oDBk0hInnHNtun8ayWr7/agmlNRWcOPNkBvTtx6GCfGq8XgK1te2clIk9spjYo5UXp2bWQgKbw+gndxzYeozuy409f8+XT37AnprdCDRyerTeJ3nD+3P1k/fTVNfIW7f+mVD5ThKqk7jpnbfZNcRhcC1Z9GG311s/cSyBEzejA0+u6Hq7dM8o4IKWz7ruQdeziSO73XbBgxrVOBOVf9eaMyfr37yVetNGiU1HCMcpFAIUIfBXGfiUGmo/OR2EIGCqrG7swZaECShCMrhYMLm0N72B3MQ69qdV0d/qRbwWx3q5lfNXXExYPwlXKpw6uC9PnXZ9S0XUvtoApzyxBGnYgMIcLZYvXXFwzOmMO7idsyqr6JWWiqKozPzdrcy6648sfvsfnHjVdf/VaZ+fnJQfy4RwXOWoVbtyUYTNFVfM5IunnmXx+qUwHGrjBO8OSeIfE45leKNgaCSeIqOCCQODnHpyGn0z0zAbY6jPl7Sluex18AtqJp6Cz310jkWzmaaNFTIxoeVhiUS9fFdXjLKHmaFquLoJ03+w7CPWNIawheosUUpzVQgKq2oZ042TYkedFKWTMl27oc5ZFxPL0sIarhz/NvklGqZuoIxJYZrm5oETBrKksBz2lxJftJeUomL0MZvaMcuGJFhGHLkxFvZwh8LaiHdmccrBeIZdewVLotpKnu6EzCwN5bByHCuabnisMo2buthPsUxkc7vSi55l427joADkpcXy7PSh3FBcz9tbDxFQ4DsivF/j8MHEmUdfLnwkMy0bbEmsx+keFEXFpai05e5LTGzv5Ij6vVAMMd2whUps1E6wOS3r3R7qk1M57rWOsgIAezfuxLz4XO46exhjzj6l021sWxKxbMKGTdi0eO6T7by9u4xrnj3OiRYpDi2ZEk1rAbx03bcMTTS6PC+7qZwwLurOeJ3skdPbrdu2djWfffFlVGk3OsdWNbBtKoMhHrr7Lm6+4894vK1pwJ1LN7FgySIUIagw64gVXgZk9sGyTCfFaZlYlkVRYxk1G1ZSU12D2+1i1PHj2bRoLUv3O2riqlT452f/5Ia7bsY7axZf7N6NdoTBWpqOFye6uI9jU+IZHBpIb7s/Ex6eglDUlshIyzaJcfzmlacp2n6AxMzUdszSPd76qPmHAMtCSulUIakqRVf8nFXaBuKseJ6e/joRXWBKG9O2MaRNyIzgN4PMWvw7ekYV049k0rAA/X/GSYlG+RY1ZCKFQqbRiA04RXDC0ZWSkj5qCd7960BKkkIG5wL3FU0FW+GfhvNclPqqGSz7MWn0yQwf4+hOzZ33KX8veorqxDVYwmJR1QJ2V/0MVU9ixlPLkOHWtKUar9HTYzBNi/B1fZB1vYfy+c493JTmJBoz+/bnxKt+zfx/vEAkEOCkX/0Wl6drYcT/ZPvJSfmxLOyn6oUXMDd/i/B6gN+CtHj1d3cRbtzD0plFWGoa/rQ/EXY5N/qqJFgV3X3PgEEMDv+Rwt39KHv2c6riHTqp7NpGsmqb8Od4iOmVTe6eJiIRC9dRUM4DrPzbCgaHoY0ma4uT4j5KJyXo9vCIO4cVX35HoqZw1bAkAtLPxrpaXqxKJSQGgBfGhQtRpI1iWkhpYyHoGarCQ6TLts1qZxCu+W4ekapK0DQUTUNRNah3tIWMplKwS9kXN5nsSJhSXcX2avxhWC4VjVWU1JQC4Ot/NgUnFHDqntUtTkr+gGextuj0jitAbUpn18Kb6D/zHlTNGbQO+eKYoGqEop27pwvuGABpqhweeQ3bYcDNxXH5QOcieYplodfUsPzrb+mp6ODuGmMxMCeBB3KcVE8oYvLi1zuprwwwYuz/XFqhPsoeGttFCXpnFomWufq6Cz1LG70bh1QYBlY36REzir9RusEEKIrAo6h4dBXQiXM5rDGuLiJNUkosdLRufldsExOdjHFn8uX777B11+5oEYkgJJzKt14piY7TI0HTVPoNGMjSBfPxu7wEGhvaOSn5e/OpM5sYlNKbFCuJAYMHMvrUSR0OO/+tr9hycDvbinYRlhGW7V2LFJIRqQOYMH0Ksz+cjd9yUpsRw/luVrz6IXG9exOXmYonLpZ+44e15y6JAmc73KhtLMVdSozcjfLgOW2+J4EdFWZo1gPLir4GUYjt46bpgE75zReR9/GXaDkdy4wbfYKeMb15YezdjMrtQrPLtvlyrof6SA/m3v060cJCVE2gaQJVU8gd1ZOc4453zisKHBddVMgdre3YuI1NuxxOI0VIjumpkHn21Vz4VatMxepfrMant6fxW/PBg4zc+QSb/noRy2bvRF1bRdm0TGoOFjD0UB58FGJfzA769B/EqdPP5VTOBeCF1V/x4q47OHf2bzCqL0WGY0nOjuVn43K4a0p7UO59pkGPxZvxH0ZaOPKUmbg8Xr579XlK9+3h3Dv+SmJG9xHS/0T7yUn5kSwptxi/7ia0Zz92RJKbvZS69IE0KhOBPfx8fg4Lps5kV1oaImAiVQFtyM5iRNiZpkkbT04PiA68JUlxzHj/U+ITE1jw5V6giUUfbGPG5SM7PQ/btrHCNhsW5VO+q5oxYdiWpJF9fI+WbcLRwcB9FA++g+wHISXbVTd1nni+3gcQ5yzRSdbL/us5bsC1JA5oz6y6dOPX7LU7luc2W9OePaiA+tkXBD/7otNtYg59xq6sHpAAJW0iLmdtO9Ruu3crbbSaII9E5QMAkrZ/zzMnLGPE0sfQVj7CVu07Vq8+lynHOGHrpF5lTFn8CcV2H1A1lj2+kAOGH0UhuggU1VksI5n6xuVs/2QRiqojFJ2DHhVcvyQhqPPhik9xWyHccem4VA23ppEam4SVkEC/1SvhDzchznyOEpdxFMT14HFpnJCZROrqOsLT/ufy0tVB5zeNO0onFSBiGUgU3N0CX23UbtI9wjSxu3FAbNNEAdQu+Hk6M2l3p1MMdjQCpWpdb1VWVEgKgif/9lcipokpITUuBmlLYmybmBgfl1//+w5EZvX1dazcsh39sFm+bVl4FRfn//7Sdn/3b9hNZH8Z6CpC0xiflcHEvByErhEyTDZvO0AgJPCmDmDb3HxqLad67s57/4aOM1jnznoFNeqIWMDmp15h9KnTWr8PMzqodzOJcek2FomUDHvIAaLaliMDYdtI2awH5ohBIp330luLW2wgvL+WDedeSnl6b+y0dM78x+Mt34srIknPzmPUyK7lOFAUGtR0vGYakbAgYCtYloJpq1i2Sn0kjXU7bU5r+BqhqMSUhIEY9n+9EanqIASKqqJoCoqqIjQVASQMzCW+Z+clwwAf/dNRKVelgktEkFLj8m/a91WHOyjg4G0sFKp3VtF7bRVVimTszH6YVi9WLZxP74WxeN6oZunUORx/RitT7PS+Y3llWyb48jEj39Mv9yJe+9kI8hI6HkNRVFxGhEAnP9ngaSeQ0XcAH99/J8s/eJszbuxE02fF04Q+uo/879KQtiBjfJjkY3vD2c9B+v9+4O1PTsqPZKlDm0idORKumgOA56V0csrC+HWNy91/ZvLaRcRvC+I5WNayT+jYDAZ5dnM8Cxhl9QIV9hTcyil37CB95XI+eO5RmqacRHKqg9gX0dn+0B0NVFT5SU/tCGJc+fkeeq2pJAfIAco1GH35MDKyWvPYtfsPAvEsfuctds+NQ1EUVMV56FVFQVFUVE3FlpJwYx1/3LKG4yYfT/LpaXxYNA8/MfQJF6JLkySzjiypYusNbCy4lymZU3HHt86y/AGDkKkQDgZxeTwdcqvhCWNZv2UtUy+7mrS83g6GxTKxDRNpWSAD1CVoJGz7gpyaT2k0DwACW01Eses5rSnItIDNN9ppHDc8GZeSwfriEfTVLWI8gyiK1BBvuanMfgbXviugIQbhC7Fx12TcOVv4Sv8ZeF1kNJSTUpeKqyGIHwUb4Wjo4MwwbRS0xDo8iUVU6HsdIlVF4lLzcMkwz5m9Wi+qrs0Flhssu/8OYvASjpiIVwsJ9TwMQd2NlVX5nUqTbgbZH2o10RLleM8PiKRIC1C7zI1LKRHI7iMpponVzQxfRsGX4geAUY/E9GJGnxm1G+CxT5NYhkK9JQGFGFXy2z/d0eX2LW1HoxsLvvyCE844k4QkB5BpWRZKJwDi6nd2obiTcWYjRnRptYHRSjGqagkhkMpQNmZBXKIbj0und8RPzKyLCfuD1B8sIP5vtxM4DGsjowy83UWjBKDGJpB2/pVHvMZm27b8CyIUkmkVk68Ooff+Tah7bRpr7iYh1UlBu00Q3agVN5ulQmpGPGf9saMMxqqn3mT9rp5887UTmTo+NkSCBp6NbdPBdnRpzekWLlnJsPt/1uUx0/REDNvk2t//hpefvhdw8dqM13hg1QO8ePKLpHg7Skhs//Y1Ju55jHp81H66Dw9QO8iJqOmazpSTplO60JHbiElrTy/QLzWLS6Y9xtvfX0qfQal8O6NjJK3ZhKLgMsIEDtNKs0yT8gN7qS0tQVFU9ny/HPn7P3Z8Bnd/g7/cjYzSgIeqLCheB03lPzkpP1kbS+gBPSe3fMy8ai+BJ/sTEwpjqW7H2w/V0ZaZasqOVVw/5knng+rwGSiRwSiqSs604wj943m8ca3AlH6bHC6RXSdkcnxS5/nJYLkTtSib2ZP4jBhG90tGOaz6pjJaiVKp64QaG7GFcB57IbCFQEZfLU0D2yLBliT36Uef/ufwCzubPdufxpm/qqiJtYSMMxnecwxrS/9KqH5nOydlx/5aQOG5Ky9wumZFx1L0ltmvxwyA141v6AjShnQRIgbSa3YRqXgLN3BVwlCqg7uI17wM86mcXriQ85kPP1sFGYMBR+wrULKE8l0ORXvD4Dq0Ay9j+T38RVlBRbXKdG8ucdZCfp6TwZbGZaxXt3HW/V+Qltm703PYOXgIMY1T6fnKNy1/q3/lPiY/cSUDln+HTM4hHG4iFA4SMSMs2r6KW+x+TNvXBHYjaUGLb4C8FRVsXF+FLRx+ONnyKrCQKJZJvBkGJLmmAnh49d33kR6JZlkcs3wRQovBcrmRugupu0DTkJruvApoIkx5v0EomgvFpaPqLhTNed1SH+bEQxsp/2w389amoKgKiq6hqqrjoKoqighi9wKRnoGqeth86EsEBrvKV+BSvbg1Hy5i8GoJuFxurOgt1r2TYmB344DYloXKD3RSbLqNpFjR1Jaqdp5ii5SsYmRwGSYCtake2+VGtSXffzofl9fDkGlj8cZ2/qwlpaYCu9m0/yDi6y85+9IroufUOTZHKB4UdxnJF43HDhsOP5FhRbWhLPa+8hZWRRHjF32Koir0A07r0IpjZbEuagHrsMiVbGZ5PpJu1g/1eZc+zsDwToqn5nHCLW+x4vVPyX7uITa89xl6TAyuGC9xgMvfdWq32cKGQX0gQDAUwutpD3ie+PvLGdNQj22afPbAAhZUl6GFP+SKp15AVVWkLbENE9u0sAwD27QofX0DojudDsCruol3xeJNcDpgKWFk2kg+OvOjdtuVH9pP7VuXEmdWM1Q6LOFbB97I2J+Npfqvq3Dvr8e2bWdip6oUxpRh6BbHTWwvMLi/qJDQa9u5OPwXKnokMifpEJqmcPzwjE5Tk+5wiE88MXz91VLcRpiLF36Mu6KkZX1sSipDjz+pg4MipeTRjdcSm+Pj2if6onp1sAzwJTvLf4D95KT8SNYUCBC77DH8y17AFiqmBBsXPsI01ruYlX0BCUYdcUY9ofQtGLXT2FDVm6vnPcOFAz9let5iAHbNyWTfNzeDELjCIRY3hln85SYUBC9FZ0lZG2rZvWk9QhHRGb3AMg1Ms4ZgvZcyFL7atRl9r4JrleqUa+oKbk3DpassDzgdwy//9Ad6pHcNxH3yzltpEG4iozL5ZMfXKLvmIlQFRe3NKadfzrD+41mwsC8h/UtW71mFEgdmoKxdG5MGazRUVsLka3hxZRk9Y6B/RhymJZFAWfEhMsu3Yfu6pu9+b/FL/LPoa3BDX6lxyzkftK788iYAIvYAKGqEwmWoWblE0jJZvPEm3G3Gl2r9AoL6Im6M/Jaq0Xn0LVhDQeJ23in9B6Zq4DK9HPhwHWk39qb448doXL2M/g9+iOqKdqRSdtRkagb+emIRikDzxhETJXQa4fLx1ro7+HrAXZgxqdTKCK8lBEj26vTPjkeVTpkstkRG6xO3NgTQLcFxlomQDsh1Q7AW4XN0PeIqS+mdv4+mmHjKU3qgmbUololimajRJb2hElMRfFVXiCpNVGm1xRHT7EJO+7yIuFDXg0pwtE3tNc6e6ZabGlQumHtdl9sDvF/WyKz5G1FxiNZMouOhhPmL5gOwfPw0pBCYmo6puTB1ZxGRCH2Bv3+2gl5bNqMqCrqqOqKKmkqwoZGQJwuhaFQKBS2nJ/sLqxHAG7N3OWk5RSBUZ1FUBel3Ig1y/as0VPwV9bg78Q36BUZTEaFvbyR+uzM5KMlx45u/H0eHGlYe3A7AwU1nctrV5yGUKHTWQeWClIwcMZKhg0fwxCsvYpo2wcYAiiIwDQOlUwCxQIlV8Azu1el3V/zpx8QX1XWYVHRmapSdVn3kPr6YNQs0jRS7gh7J01ESE2h48X3QpAN6VYRDFKc6s3Yr5EIzdsLsX4HqAtUNU34HKV0rsGsyQqmWS787VqGoKrEZTtQn88VH2223tH4jg8uL0BQNRXUqmZqMJnJSe7YMsJrlYnvi95z9zvnMu+ar9t+QouBKdPqkM2+ZzNcvfc6hfY24EuPQu3C8it3rjuikhGyb4nA5tz7zPD4ZS1EbzhfbNCku2EPx9x8zad8TZABVJLI68xcIzcW4c2/C49GpGJ5Mz601zL1/Jcf/cRI+r4YqVYJxHeN5ew8WkFzv4EcS9sLBvXsAqL8wzAUndJwE3ZmdyEsBi10xTj+YMeNshltBYpOS6T9xCnoXFA1CCHamf88FQ85H7Y6A7n+x/SAn5cUXX+TFF18kPz8fgKFDh3L33Xdz2mmnkZ+fT+/enc8wP/roIy644IJO1/1fsdnG8WRRQf8+vbCtMMUF+Ugp2W5lI4TOJPcOvs0SDI1dSJpRS69IKQv0Y9g9aDBBEcf7H15NttJAT7dTJSSlTX7eIOoz+uK1wcbm9T5uTi0xiPUbCCkR0uG8EEg0U+LBQz/FZrWQvLfPEQG0hKA1KN7MFaGg2iaxRwDfhj0CQpJwUYVT/hk9nqdJMl+8y7Abx9Mz9VH27X8QV4LD63xw+f1YH/wdoarYKIyiBn92CumXXskN275k9LBYbry0lUBu9ufzyJ+1Db0bHMKsXbMo1xz21lJ52KB62iP4t/ipbbwcPmm+vkOMm1EPvjd4T54HwN7qNLa7GtjbYzrKxFFcPbEP5Qv7IaXgsptzefOpQkBSdXAe2+Y/jLrXGdz8120hvt8Ex4kAhNvD/i8+wzZNrJw45POfAiA1jaLC5czd+DKKUNAVnXDFQfKC27mtXxw5fQdwsD7ICfML+NWxAzn1mM47lNkLV7IkIrjx1JNa/jYWaObUPLRiBY2fzyLjkQcZf9JJnbax5rJL0Hfs5I5ZswGwbQsrYmBEwliGQcHypXz7/utkPnA/mTNmOiR7ltWObK/2+rNQVEnmyHcw7TB/iDRSE8jHQ4SIFaapuBRrVRJiuIbwxRE0w7xrBDnFO4lexGDZNm8dqEBxq/TpkYAp4ZszLmb8uuVUDB6HtG2EEUGJRFAjYWR9BTFWDSuzhlHgSqSiEiwpMKXAlGDZFgZxBGwDAxuJgnGgACEhxRbULC5FtSWKDephhVC2gK1JPTi7fAN89Dvgd7gAF2BqChtHH0eVWcmN736GETaJBEOEA0Hev/OPFGz6kpd+92Wn33OPmEFMSj8TPLA1v4Ctjz/Ssi5T72QWq6hdVt0AYFrdluu3taS+vVkzfAo0NqDV1yBMk7TwHmyZhZI4jFC5F6TiRDylinRmM0ipAgq6KIKtbQjG6gvh0tldn5riZpCxg7Vfv8r4s65j4gWn0njKMUTCBjUl5ZgXO4DRcrWRGXM7SktcEnMet59/LwB9YvpSzkECdI1VA1j6xWqKDzgMxU4qsLWPeHPVWt4urUEAv3PHM67RzSdPryZWUxzHTIjmgh0QgpFNWayNdRGI95KvamxJSCC0dDa/OfY8Nr57O2PzX6UZtbd9+iz6jz2RiYc5BqMuHszBrcsYEVA48MACCnpVMjKQR/GxBjtXfUP+vE8JhxMorp5BOLMWN6kcc0tPPDFpmBK+e2g9B5eW8OGmSuLTPM3S0CAhXvo4qynIWUqYnFQfM049icQ4D1JKCmsC7D9YjiIEa/NreH7RfgDS49xM7Z/Kozf/jdTYo1OJ/t9oP8hJyc3N5eGHH6Z///5IKXnrrbc4++yz2bhxI4MGDaK0tLTd9q+88gqPPvoop53WVVDy/44dpCdWz6lMv/yX1IfrefzJO0gPpYMGUylgiNmX22sGQ83p7AhaRPZ8w5np3zPi779m/dSHoYfF2FnP/svHf+OPn2OEA/z6mV8wBGjO9tq2TThiEApHCEUMwmGD+Z98SNnqJcR4OyNrbmN9Ugjm1/HwA+076QeumIm5OcAz17+DV/dimr8hNncj6cNXUKi4CVUE6TmsP0La1EoLpd+JgPNMHp5OtaI4hM7I0JotjMko0ZdV7CHNspCW1ZoS0Fx4b3iE2oe2tWyfOLaKqds1igdU4q8ZyDL3frYYjRh6DgciSaR/v59yfxV74woIB3vz5dv5qFInp3gpPSq/g35e6vv52KilYN9+L0IIBAIG9SRQtBPec4jDTthRgBd49gyF4AfHs044TlJiNIIQVsDISOMjt5scWsuVlW44D0KW7PahPRrchlVXh9am2kRRVBSPih4NretRoKM3IdGJsnUycNZjo7jcDE3p3+avrenMqvAqQo0GCaMziOvhEPVdflgb/1yczzHp8TxwcpQi/ZSOVOkA6758mXHr/0QAD8mDpnPV+ZehHIZdadi1kvgPTqP8uGdYKnpR+OKjXPDM6/RIT8OSMoodktgSLGljWxLTklT6Gzh5YxF/cw0gkPsmkQV3oDfVE+45Eteoq/H1PpPwor+AUYumq2i6ijfWDSRw1i1/4fvX51BfL5k8SUNISfOgIpGo6xqRZpiZ/SehxjsgcjvqzOYMbs+2KqWEbpTHAbDMbquf2poWF8vpH7/W8tkfNvE/2JfqwckMvmhaN3s22ynAm2CZ8Lcj8+vmXfcRPN0fy2glxYtLdCKGKRnJ7Hj1HV74ZB6HEmJ4amB/LNvCsi1M2+SBfY+wvnFTy35VspwUfza3Tbit22MWbFmLlAY5Q05GOwzk/WVpFTsSsxhZW8a6dIniUTANi6BpE68oLcSFSnRyZSVmM2zUcG6a3p/7F3/Op0Eff7XSuc6ywF9NkciiafqTxKblMrTf8E7OxqFwWH9eLT2+NEgwY8nLT6Ystobq7+cR/9ZCegH5PSZBXxV3WSqNcVX0zh5PQmwcUko+6uMj7WCQ+rIwvfwmUkRrJaLO1AQEiUBiQR3uIX4Y6qG8Icxxjy5Gw+J89xaK7QRUemGhUNEY5tMNxWTEe7jt1EFH/A3/t9oPclLOPPPMdp8feOABXnzxRVatWsXQoUPJzGxf/vTZZ5/x85//nNjDZL7/L5rbF0tFwObZj79DEYLMnqPoZZZSeMDARMNSCnDUaKDEsKmdMY0zboqiwS0L0Q3/xNGYZTqlfIeboih4PW68nlZPOz1Wp84Od6hK6NimhVRkh7+nHT+Oqk0eRDgd1dtAJNSbhvzeNOQ7s6m6lHym/uaXHfaTUYrvdn+LDrpqN0A/CwtNcXOWmsIXVPPt2ic5ddKtrdeYkETO/VMILZhLzSKFpi02t/RLYOOnQYq4lbmTb+S0OJOP/E4YudHWeGSLBUouxBg0NmlM0f30v3YA/U9YgCsmlY3fvkjg9a8ZmJ6JruhIyyK0Zw+K20dtrIuqJj8HRqXjG5hFcso6IloM0/ExJmUYl5z2AgBPrPiUN/bdg8vrgOqaFXvDsuN32mxhKdG7oUQ5GidFDYWxPV3PrMINTlTKnZDY9XEiFsLV9T1pN1d9aV3fQ5YE9ShSF5F9Szgkssi6YyNjXJ3jP6QRct5obsoanChXjEuPMh8331TNr0pLzxdBxdAFntwp+PqdiK+fA65sCzm3bIPOuso+YwaS/+kGQkEPo69vD8os+dsnmMkuhB1iwiUdtWM6nH/YQAilS2VxgAOl9UxorOOL6/+MNEz6/uJ8hh0/4YhtA5iWxEeI6k4qVLo1VQOkk/LpwtZ/9SoJm14iDpBa52mHIdPGUbm4ngwUTprUXmrjo/0fs5v9FJYcoGd2H0xpYCsmmw98RErjLhTFi6Z70V0+3J5YPN4k3J5kLDNAQkYfLrrnpnbt2VISRtC/rpxvz3UmWh+VVnPTriJeGZLHSRndc0n95fhzyFzxT/4WiiBUFWlbBJUYBk8+8mT75+PP4Zp1f+KWp79ExbmPmvML/sf/xMjkiRx4q4weejGaJXnt1zcj7VpAkiBNTL0/euwMJv5lSrt2pZRsrdrKdV9cywf1j3Djohv5ft1mpKVxf9MfCMYEKRYmfdVq9lmplNrx9EjWeeuqKeSlHJ00wP9W+5cxKZZl8fHHH+P3+5k8eXKH9evXr2fTpk08//zz3bYTDocJh1u974aGhm62/s+1spBKUuAQoarWstgDAGiElBDZZ47iT+F6QoF6BswtY3pGa+cuLLPbzutozLIEbu/RoeHMSAQU5YgshpZt0RnL+a+vuo+nn59FcH8jv3vsAiryy9i/8QBIyfp5fgJN6eTvLqbXwPaEZZKOUYSmQidXa/r9mHE+FF3rUOpZ6fUTTw2PnvYPvvjqZ4QjTR3OSWgqwb1hJGmAn8mXD2aAOQt3/vsMT4K/Lf8jKHDqMT15/JSBhEyLWJeL0XfPZURfyYBhN5GW+RqumOisMgpuO+au20lKb03N2LbNR7NehC++IfdP9zJp+DS60k0OR5WDvbrzW1dFKe67i6REbNmJrnGr2UdTAWOZSHfXg06oqQkhJe6EhC63wbBQuqnUsC0D0FH1ro9jSIn7KKqS4hoPUJ8wmNwuHBQArGgforrY2uBnIPDyjdcw7NqbOeuYY7rczZTOd651851LywC64FmxnKqlDqfTmAqyEs+go5ug2UHHyequNLg6pzdm+S4S1iwnvbGSPbZ11E5KxLRIIYx0/YsDVv4y2PwBjLyow6qx61onBAm5XVeLBCybtE5wIxf3uZA7Dv6V8779Oa8d+wqDYofyTXg27wUrGGfPdyJUFhCiHaHT4Eugbvfodm3VNzUxZtlm/ImZjKhtjeyHolFK91GqXZc0hfGIMGX3DaC/1UiZ3uPIO0XtxcvuZu/TTnT50M+mITITGX/pzSSkZOEvq8UX2UFVxEet3yEt1Nxp5I2Yyv61X2Ibuwk2VAHtSQObjCYumXMJCTj3k4zecx7L5vhYJyq3aHcNZsVaSsc7fExxPos+af/5AYIfPPJt3bqVyZMnEwqFiI2N5bPPPmNIJ1UXr732GoMHD2bKlCmdtNJqDz30EH/9619/6Gn8x9labTjnjcnmplMGsuLQGq5f9Ot262/o/wfej0+jeP8WGr66n8ZjbmpdaZtHVbrXndmWgqYf3QNaW7wfXVjkv3yJwz2guhCK7hBAqS7ns6qTXbGa1brKQ6sfwqW6cAs3SXU5pHsyCBYoCMWJDKT3yiS9lxNlW73wC/RwLF+/uJnrn2p1UhxMR8dIiohydsz607Wtf0M4s06hgBqGU2A/xUyf48xmfateIrTyOUxp0yB1pyIJgVr/IgBVwsvehz+mz2W/YvOe93lozU3sU5xOKH99Bddsq+HRq8byxAerCQBD8hy3QNXbgOmk4wwoSuvvsnTWXNZ+/jzN2h7rnm1gvbKQ8KBSbrnpkg7fc8Ryrs0XHcirIs7nCqNr9WFDSkcPqQuzGxzuDBHpGvAabmhA+roe8MP+RjTLRvV24xRETISn61m5NExAR+kmGhdG4u4OgwGsnf044+2dbMs7v9vtMJ3rlaqbu089hrciflzfzOZQYSF056REAabdOSm2NBFddJW25SSS2v0tYiAUFc8gH2nXHl2q247qaIluqm7uf+1u4G4AFk84FqEfPYGZGfGjCPmvOSmqG8INWF/cyK6mOISqoqgaiqoTbKiimZFp89QXGDn22C6bMW2J3knkbOZx5zKq/3iu/voqrlv8G56f/Czq/jksrfMzaeL3eNxxhMN+IkE/oUADoUAt4UgdBw78EUVvBeK/sHQlz9Yb+GMTOL+hlGtHtzpMLUKT3TAet7Vdog+mHWZ32gxcRj16n6NJkTmmxcbi++dcAmefijZvO8etXYaiKDTW1vLMi0+RkGGgWiaBtMFIRQFFY0vtAXzCRkiQVm2HNj2ah/P6n4dsMmEv3Dz2ZtLNRSTPv585wVkc6H06wePPJmKeCcsPcuv0AVx3XN+jPuf/zfaDnZSBAweyadMm6uvr+eSTT7jiiitYsmRJO0clGAzy/vvvc9dddx2xvTvuuINbbrml5XNDQwM9ehy91/qfYo0RC0M6ZWkTckbz2LQXMA0v3+xdyZKaVzntsxO5st9dnBt2Bga9vKZ1Z9Ok/FDX2iJHY5YhqSrax0dXXuzwnagqfcZNZOSvf9th2ySzAlNESChfhYKFIp1FxULBeVWxqU1OZFVCPKt25QOQXd+fs3bcQB0h4kijIb20Q9s3PHk67zz4NcHaCPPn38IiMYgmZSj1EaejrzksNJM75iyGVKSiDU9EcQts08K2LGzLxN9YQIX+OVeFh9DU04Xu0YipLmZQbB4VoXLsUBH5sgcxejJIizjfhzQwhUYzhYGVPah/u4BU410eDEa4Rg8yxOOhqCnErlCI619dzWZ/iD+PyGXiED+bi0BpE8qW0cFNiebQ6sqqWfv5cwCoejLuGZOhzMLeD/rudCrqa/C4PXg1F3o0dRWJDqzeKL4iA5tfuDfgWrSWvyxRo+XeClJxyr6lUOgvBIcSU/nbvUsQqChCQRFq9L1Kn50bGALUX/cbQsOmIhy6ToSmIXRHxTi1Kcj2JJt73v89arNOjKKjKgqaolNWsw5STTa9tQQ1xocQCoraqpCMEFQyhKaGckq2LMHrcuHWncWluXHrHszdNi7CiLCJNA3nPA5zBAwJrm74SdbNm8X4rffxfcp5TDzrFvyFOwi89jOEtLAVHSk0bEUHxQXhehIUQHWRm5TInVdcwSNzZ6MewQlqTrFp3Ukz2AZCdO2kKIdFUuxAc1Tk6LtXOxCtAjtKAj3dNFA8nadWOm0/5IBQlX/FSbmrgj33DGeAVcjQ737R6SY7zp3PyBGdMyo3W1dOim3brP58OYNrR/Bd7ndcvvZqEoQbnwqr10wiHM7k5JO+Iya2PVfJ3l234Umv5JvZ0/jAex6rGU1E0/m5v4L7px9PfBsnOxT9nb1HEUnZWVbGamkTVDwc95t/DQeYNzCPTapOpr+GbV/OZ8TZ09m/fj2GEFS5XfQ1FbwCmrCpFTqWAn37HUdSfAypvXt1aE9XdO6ZfA+BokZqF26md2JvvB+eBALOSHoPrrwdEnsCcNcZXVM1/CfaD3ZSXC4X/fo54e2xY8eydu1ann76aV5++eWWbT755BMCgQCXX344VK6jud1u3N2Env9brDFk8uqyA9x+2iA8mocZfR3PfFrvITy/KoP38+/nzX1/Y9vuGP4EeJ56m0Uff4wQChkhP3sSN/HpGzfz9CUPU/J1Ef6NFaT/rB8Zo51Sv/VzlrNp3rcIRYkOKqoTbVBVFEXBDPmRdpCwFcK2baqwOLBgDvMXzGFMn8HEZWaS2Ks3/c4+l9TERGpLa0i6e3+X1yNtm4Z3j2WoDPHM+XMIWSE+nvMt7IDj/9ATW1j0yBjXYT9NV/HGVWCFFOrsFbymX47biuCODvpNse0F3BSpkhszgNTzx+FJbj+z37Py7xCqoV91IgMHPkx877Et65btfZlI0SP0G/kKeSntQ8K79u0l8kop8Q2CRsXC1SeJ1RdPwhPnZv2OCs57ey2b/CFOifPx61+M5MAmJ2VZXrcWs9JLTEwaoaCD22iOpAQaHSdyws+uZ9pFrbPnl978hNDqJI558HuMlv7RAmEBKSiea3FFnRZ3KIhLWJDZk5RePR12YMt2qm9s2/m8dSPucAS9OuRUeigaSbm9sG1nu+q8YTTl55PgTcZuqENapqOhYhnOq21RG6vw/WCDJYGlSCGxhRM8toV0Stb7O0vO94mdko4B0P9aSoMHePf9jqk1gKeUHZzjuR/aZHul1JA4uk0SDYNn8JiHgDGdttFUsguASde/ilAUGnevIFOUUS5yMF1JCDuCsA2E0YQiJCXkkdhnBBDlIrFttlXX8fCqjeiKcBahoKsKuqLgUgSHIn5A7ZDMsWyTXeXLsbHJry9FCNiydxeartEvpyeuaBTDsiXK4ZGU5qjID5AVsP3RfY6SQE83Iyjd4IoONzMc/Z3+xXTPOqsvA7RCQlJn/6nvE5OYjGWa2JaJ6oll8MDOGa7bmmHaqGbrd7Xuy+XMWbcAO4piHeDqxWDvb/i48XNK1VJOTTiBYKAar28Na9Y+wdRj/tKuvYy0CzgQWMt3SRP4VhxP32AJ56hu/nha+1QJOOXuAJ6jqI6asbmASJS5+o03PuScn51GUmLXFAhd2a6hkxm5ZSkHv5zHiLOn03vwYFi+HIDzbr4ZX4rjdJXsKOCVj94gZegwjr14RqdtRSI1TF3xPYXkcEeuzrUuFe4ohroCcMdD4n/fxL7Z/m2eFNu222FKwEn1nHXWWaSlda2I+n/RTh3aUVch3u3jjuMupCHcyN66/ZQMrOHjY5aRaSWQ7k1GWhalWfEsGxJkpzKfu1+4ievKLyBJ6jR9sIuqFSUIAQVbNlBXvx13TKYj623bSOksSBsrUkXflL6c84JTVli9dQsb3vwHWw4dYMOBnXBgJ6xcxM2nn4VpRNCOMOMQikLQjhCruUmPcRwlrx2Djc2Qfn27xbPomhtdVxk57j3Y3MRzAxROzhzD4CXfEuc7LIUQJZ/SDsvVN8xfTyB/L/SBhpQDbF59Bdnbz3ZSQELFNFdBLAjZOsjatkVFTQkr18wl2DCUoVfFcvzE9jn9cKS1E02yIFQfpnB/KSRBce3DFNc+DEBNSTyQg6I5nVkzFsQb3z4HfNF50/lAW4OxDcZlQ98+JhHLImJaLNyiEQz2bvmummf108ZN4qRxnc+GHtm6Ba2ugpjGJiLBCkDiL9uGOyYVzaXQN/tEso69j4QbRhGX27li73mvn8VATz/W/eKJDuuklDw/93VerniKXz48AV1RkbaNbdpIK7pIyet/24x32CC+vigdfzhMxIwQMSKEDYOwaWCtLcWuEETGPoqwDMdJMg2wTbAMpGkQ2a0R46vv9BwBhBWmlgSSogOLjDjRgLhrPsOXM7DL/QAMw4lS7ZAqHweb70WJA25oS9zmtF1RfIBDiamIqPDlZ9uf5YU9i9o3utKhUThOnclzlzqqyLYNymGaKs2RFOUH4MjsoHO+ivfoUjguy+jAydOdyUAtYUtFaP/ahHD6Dc/w/dfPMLnoVRLTs8np23klVnfmJ8K+2nxevH8DEkmTGcQlNI4bNhlFqPQe1Z/0vtlcw28JhAP43D78/mpWrZ5AY2NRh/YmnPhXVmz/hq8rssgTZTw1fgDjUvvS6/avAThjRBbP/cJxgJvB6N4jqLUHI2EiLjd9CnYzacNiasqLeH3uOwRi0ugz4zx2b99Jwu4l2AjOevAFBvbt2jmY8cLfKZs6GbuoENu2ScjJ4ZjkZFZXVOBJagXvxmckArBw9/eMrZlMTLLjEJWXl/Piiy+Sl7eDnnnruSig8ka1G28fk22lYxjc+Bi+EUPaCTz+N9oPclLuuOMOTjvtNHr27EljYyPvv/8+ixcv5ttvv23ZZt++fSxdupQ5c+b8j5/sf7Jlxnvom941iOmh6b8CYM7uddzGSv467gnOGdoKSJ64Zge3bLyVb5KWkxvJ5NyaE3ELgftQI7aUTEgaTYmxkxtee6HT9p+94HTiY1pv5pThIzjl8Wc4yTSxI2HeuvoS6myTVy86myZVAHqXJUGBpgrO/vhkyoXN8WqrHoYZklha5IiAW9sCoUpC0eiJR4FwlIhOO+yBE1FtlcNLM+u/OkCK61KSt84kf+pfiKQHyeeD9vsGofHEC1n1h6vZbvupmbsWBYFQU3HHD8Ud6kiy5G7jDCUFLCofWs1ij0pMzOmccuIxVFTPQ9UWU7M7EYAXr7/GYeM1QqjAIxv+jr/4SSeaFf3XFPAB53Hh8F5ccMLQlvZP3D+fA+Ewj778DwCq/DZe6BanoWjgi43ld088jWmYzHv5LWpLy6gt3U+gPkxlcCkDM36G6u360Takga50nlYQQmCYBkIqxHQzezQFpMTHMDRaXny4fb93JaEKF76zrun8HAwDedc83J6uB2XFDBIRrevtSHTw93bufLU1K4rruS3BxQnHDidsWYRNE8OyCZsmYcsmYlnsKVzGivl7eTtpHo/te6xDOy9M+zOGYWAEUpHSyyPrH8avt0aPbMsRpGt37GrnPP0bFMKFH5N565E5olrSPd18Hy3tWza6ZTCn7Gv+9uZaLGlgyjCprh7MvuAJYjpxXsLbF/DcnskoTzzNUteTKEJGqUJECwedaKEPEa3v22wjbZNKBjPhX6w0DAqLkAiRGpeMEIIMIcjt0YOJ53TEsfjczmTF63XuQU1b2GGbffVF7GgK4iHI6uOdKp7GUKuUQEOoFdsVjk4APIeB7sOmya7SMjRFwa2pNIac3+7K/nmcMeM+3r3palRsfP5Kyj59iWYouYLkqz//hs/HncHgrDTSY+KITUnGmxBHTEIs3vhYvPEx7O85hEEFW1k9agLZ771Pdm4uZk0NhzZupOdYJ/Ibm5LAMb3GsCJ/Ax99+A29zz6FPnFBasuKAUhKPghAYcQ5901BSV/PKkq/WkSO51S8g/4zmGP/VftBTkpFRQWXX345paWlJCQkMGLECL799ltOOaVVNv31118nNzeX6dM7htz+L5uwbFZvKuW5Ej+qEKiqIBDxE9Aj6B4NRRFoimCf37khD6cP7zNhCJ9PmMN1b13FsuQNXH/LXRz4ZA+qSyVYf5CE3V4mn9d5vhjAkhJFdzkaKm2cCCWqKjztoss5sGgBtmmys9J5OPhbNAcslJYlhIuFHjdl6TGAYHm4hnPfPRdTuDlm88+JNT08/Pw6hCJQbYuMA/vI1KNMn4pAUaCiIgdfxg6+2fIdiKk8sE9w7B7nuhesLOK9gMCtK3hcKqEiB86vHEYVLVFQXaXkPHIxufY5YJtIy8C2LbAMSqrz+W72kyTb69mx5XvqDkVQEHhPH0VqMI/KjZAa0/HhHjM4ldfOHUXN3lqGSoUCfx2UCob3OJtRY0/Cti9g9eoHGTmjFyve+ICk3v0prSohbKn4VZuYpGRidRUbC0ta2NKm1uV0fGZcY7tjuY16wMM/C51BJYEg43XIVruOLliWhTsKotZ0jZk3XN36ndiSb293cujdVP5iYuJSut4gZIbR7O4HS1V2L8qHGSIsXHQFrY2Ene+kMw6WZhNWGKONk4LhpNQ0z5GdFCMKHFY1DV1V0VWV2E5AvGZTFlvCW5iRM5ohffOwZGsEMi9xCGN6tAe+PrX+KfQ2+BRbdnRSPCPyUOYexA54MMuCHI3ZUYC4EnMUkRTTQAGUmACa0PGoMVTYqyi2DvLmxnlcP+lMTMtizaE9ZMYmkR6TzM5QJiCwEQwZ2sO5RltGr9VhNJYtzMbRz4etq6gLs7fWxbrn3+HAoGMJx6Wiqk6/JQSoQmBLh4vGtGwsO5pGtCSuBoOw1FCQJMQnRB0ghaaGRj5/5i0i3rBTKRg9vmlZhBWVxLQMVC2RmJg6tmx9G5ceh9udRMSTyjm7aqilDz1EK/bN59KY2i+Vfumx3Hl6K3C2LApKjzsMA/XbeUv52tuxH0jxesjOSuNPH35BQ2MTu3bsYfPuAnr1yGLalNG88eq7BJZ9hrbuK/YCenEVCVX1RIAI0Ax9zVE0gqpOYsRP4IKzGbBuLdr69exYsqTFSQE45cqzWHHvBgrKd3LHbidK+Li8hSnHVBIK+Ujc/hg9qr/kPncCUyadi1abgvesNDwDuy+n/m+wH+SkvPbaa0fc5sEHH+TBBx/8l0/ov9UGRWCtv4mt1U3YOAFnE8nhGq2Kp4GY3qCa4U7bGZc0hudrXkPVFPpf5BD07HnfKWseeVLn5YiWZWGpKptLs9h57TwU20C1IyQoDVzwjysBGHD2uQw4+1xs22bnxVFeh4EzwQyBGXYWK0xhRYj4oE62FSZW64kqXETMCIFgJdUxlcSGEzBLgwhbIv0BzBrJIV135N2FI/Ou+Px4kw8SZ+wF11R2SZVdkUY8QEVDiDuX7mk59yeVCLPcu+C+FShR0jRFCIiXjK4X5OBwvaC4QHO1YAvyYlP41S3vsv2VwfReXkhttLpoxOApqIfSqKQRZMcBUgjBSRNyYIJTeXTfPQ+AAF+UmEpRFEYOv5XlX28he+IfUBTQwvmEvGU8dlfnonOfb9vATfmlxPraAx2np9dR6/ew/IHLADiw7BPeXlBCYnzXuAHTttG7wCIIRdB7xFDYBlo3aYOgCONWug77B80guuymvNi2UXHwRV2faASDrtsINzsp3aREVCvcLpJiRcGfqufIuIqGesfRq6yq6nY7G+d7GN1zBqcMOaXbbQEsTNQ2USjbBnGYk6J6dLLvPoWiP8w+YlSx2WR01q/4jiIdE+WgOXHAVJ66wmGxHfnaidhaJV/vX0CqL4nFBatYXvNWyy7ptW5m4jwDeVfdRo+Mrinuu7J1G3fw1TOPklC4iWDQw4bUcQ5xXVSyoa05rK5OJEZKh9M6UUiyFYONBduRzf+k82pKw2lCtPwHQlBQUkZa+lAGDVpBZaVTBVpBBjeLF4B4Lo/fz73DTm45rqoI3v1VJyrL0fNLOux+KzdMfGqIv6e4MGybiGWjqQpnjhjVsk18XCwTJo5hwsRW7NRvbrga89rLqK6o4t1bfo0cPATromsINjQRrm/ECASQwQB2IIgMBcmd7fwW2447lsxRo1mtqqy+524U4KqRGrlbHiedy6gglbIlx7UcZ9nIQWSWnUxycSpXjb6NxDP7oPj+5xTP/xPsJ+2eH8ke8MYTe1wm8Se3Mk0+97cnyUrM4OzfXIhp2ximxbxt87h3J6R20VkFrRCqrVBTU0VyssPZIaOhTNEFMZYZiQCSNF+YPr1DmIZNaUGQCnoSrm/CndCahpKW01n6bBMuntWhra1P3UZRo+DbXz7c8jfbtrnvvvtwHd+f66e3EjUt+uRzMv9yP+nLl5OS2orM//jad1F2jeY3Nz7OdVJi25Ky/AY++mcdQ4/PZdC0bEIRi2DIZO3cpQRqI0zIHYGUEjuKtVlftoNFSbDyz39Gs23OmzmTPsd2DBvnT+1PQ0E9dbEREptcYEsqyxqoT9rGx59twTPPjaIoTpVMM+g4qvRsS6NlkAlFNWxM0+L12xcjTBe27gy0PjMXEfGx9qvXMYN+hwDKsgg31BGfncf6/CpgIDtW7iO13sbt9aK7dAqqI7jaOEpmNJ2hebpOC9pS4nJ1PZBJw8aWFko3EQoDE3c35Fz1jU2oZtdOjmHaCES3ysFYIQzRdWfqj6Y3rC6qZsCJpJhtIj5FW1eT7RU8e/kZqC4nuNesxTPspGM49nzHSSxcuIHA3GJm5FxFJF9l6wcrGX5R51QIzRVW7qPEaliY7SMptqBL+Ja06VBT34XZzRw5MUeu2JFRJ0VpQ8t+86g/8/i2mykyF3D/xgXOdlJwZb97qA/XEckyqaxezMTS3lR/vhW/7wCKqqJoitNvNFPFK04/4kQ9neipOzGGzAmDGTd6COPeeIO//fJiJo1MZ/Y1R2Ckjtqn6wv5bUMNi8cOIim+/fWVrd2FObsS9ZwUsia14rDeePoJiqprufOeezEMg3C4kUikiXC4juf2NbY4HX8eOh2f+8iRtXAUOCuE4JFlq3ijLoxiWzR6Ehjir+GCGR1Lxe2wRWhnNZ7BKSjujs+TprvIyMlGs21iszIZdlJHvjBw+t/Zq+eQV12POnQI/RSJFxsrJZn8yhre2RQgTbmQCjoy+45MeQWtVxYcI3D3Sfg/56DAT07Kj2Yy3FFl1Za2U/apqWioeFw6bpfT8eudSNYbpknflLFc9e1YXsr/kj/f55Db2yETW6qoXXCpRKKgvOxJQ5l0mcMavOmlOVRsgvl//ojTn29lf1V1F5MrKklK7Pw6bNsmydUey2FEO03vYbwNVsgZiFyHzfxtCc3jm4imvlQhcElBcryLntmtnc7u790U16ic9qv2jJ79vp7DoT27MUyTNX4/5fv3d3BSlt9/D7sabUiOIzYo8ee6mTTmFJaUziVSXkuiKxMhFGxpY9oGtuE4QLa0kdiEpR8pLVwihry+TmTls5eXIUwXCf1MLr3VoWm7+c7HSGmKx/fHZ1Bkx9/Z23caDB9IzZKv+P/aO+v4OsrsDz8jV5PceNK0aZO6U6GGVIEixd1h8QV22V1YWFjcFmdxd3coFEqhLaWl7u5J08Zdro68vz/mxqUpP2i7u/O087k3M3Nn3nnv3Jkz5z3nexb+0Phk3x2I8yQDl1rt3bELgCdeeIOIHEFYhZcQkkBI1jCdW3aTv2Mz/77w9GhKcNS4ik7pZg8GxI+m4IHF0cJx1k2c6I0HVeLE4AQ+Nb+i4KsKVFnFoThwyA5UWUWRFBabc3ArEqsXvWOlN8tKNFvMSnUOBA08KcX8tENC3pCIW1XwOGS80dcYVUHxF5Ng+NH9fmSnC0lVmnkVtpZbw3jX/QTXzpvRrOpuQloMa/4yCV+kBFe0FlP1C/05IqYIU8CQYwcTCYYxDQ3D0JE2JuGYl8qaNZ8gyTJqSMUnJ5Eft4fESCpibYDVJbPBIdP1qH6k9WvU5ymrtXQ2PljxJLt2vYcsKSiSZaQqkpXWbaVpqyiyg1JnAf6S/uR+NxPFoRD0a7gC5VR/+hF4MizLCUCWMP1+ZF8q/mVFUdFDKWoMRD0NSvT7ccgY1TrCEMjevRspZv1QVpPf28UjprC5/M+4VRfjMoewpSyPlJgELhg2CYBAOMDWuWmkpKZATltbjUr6W3totkQnSEV8Hkn9e0T/llBF63iu9qjXKGkz/TgaOyS18MqZpokMKFG5BLfbDViJGE8lBhmzdAsAe6pDJHRi+G9ZteWF6/vjMvSIjleYHKGaGIafMwa29ioFNpez+a1FAPQ/ezQxI9JbrVOPoGPxxHBVJXuSfeg9s7nglbcb5q9YcT6O1XvYvm0Me9xdAThBzOHzivsp1AZz2sU76Hr43rOm/tuxjZT9hAgb1C4oaOZJMYTZSj1VN60fraNFcFpVSYD37lwMQIwMlHbFiOgoThUzYjQoZ7ZFJGosqE0C6gaeP5FfVi8h18hm9W1Pk+qoRvU4kdwu0gJB3IltRxNsr3MTNhVCVVUoTieyy0UgetGMaRH0Z0Rd+u4WhbhMISPLzS9y1SXRgn3VzUXIiirykB0B/P4yFMWBrDhQZAcDTjiegdNOQA8G2fXWFQTCS1n/kfUE6fJk4A8WsmRdFQBeIXH1O18h119IJKuvLrnyDBI7yEB75ZVXKCws5LY7/97Ynk1B4roqXHhTY8yVKZkkhvsw/4iHOfzsCKl9+6JrYcLBOhTFwbnA5GqJpU+U4klIoc/F1xKJaOz48CmyzEajJS6jjsqyAD37WULahmFYacemgWlYKcjm6t3IkSoSu2RE9WIMzOhyYRrEqYnEOZKsysm6FUtAk2rKCLjQnEaNUscP8o+YkomJiSmZlvtdEuCA3h6d8uDd7fZN1hRYsPSP3P5O2/o9n+pxvFn3Dty4pGGejI4s6ciYFPkk9OxYRnpNuqWmNtweZ20sprrIz8BHZrMpYBWPq36hP/HFRehOJ3XjzuWYKc0r62655yPUkI8wGugCZKhMqGDsLeex/r1fYKsfZynEaio5762kYlShpVysypg5JSSbscwKF7OieJs1FCtZt2mznaEatSaBGV9Fz/XYLLpV51Fwe+u+co+5BiWxN5WfbWu3H5tsFWQBHXiW6qn3uDWtfCvLMg9NbQxSPq5f87TuX175iv6iK3UX+ug/8BArY0vTMTQdQ9PAtDyywjAReuP5VL48B9dqMHTrN2MKgSkrmLO/4c783dx7zwN7bW99+m9bmjj1HqSWtXdM02ym5FtXV8q8n89FCAmTOPDeZfVBJz1VJ6cl8Fp+GbWKgx7hSi5P9HD1pMntrv/Jt5+T47Ji847cpnD0iPZF+QRSu15sgNq8POu1pFF4bsWD9/HTmhrAh5fNCMkSv8wetZllxVZw/bJ1PzF1xLF4PD06dYz/rdhGyn7EM7B5gJaJidLCAq83UlS5+VcTl+zGKcG4GAXZEeIT1yKef3gT5116PkYgjCE6kFKP6jY4mhgpLl8Mkw4XzFtgoH75KjWm1uwzsrdtoydsWu196N//bpgXLVJPQgtxKTMcQVOUVhWMhbCq2TebF31VW2TxxPiWMXjEPBYv+ahVW0T0mPv1sz5dHJ2vhRU2vNeYddL1ohvI2V1I7+xMa3nU89PSw9OS/HzrImWaljG58IdVyLqLLr2bx0TcftMVLH57Hnkb4+mW1o/knn1bbasXsOZJyIzxMGWiNWb+/dxnKS9rvBCrzgBzus1h9emrUdrRc3joghNImDyCS65o++aw/umviewJ0+OOSW0uF0Kw+9b5XNDtQh48+9lWyw3T4LvvzwWziMPHzsDQLeE8XTcQpo7AJBIpZtP2cxl+VCbnpowlZJgENIOgZhDSTYK6yawZBklaLZOPFJYhFU1hNnQT0zBZZ3jRB8Tz59RUjh7S6Nn4un8aT/68g7pIhMfEuZyjz8YnK1SnZ+A47gkSerYuMuBwykjZNQy65JxWy4Zc0Kg2u+6uWXQLJ8AvjcGsIxnISOCS8cfjGzC42WeFEBiGhmnqGEYEwwihRSK4dXf0Bh8hUlaJ+nMlnj4lRE76BjkmtiFGQ2gmUmxaQzFEEb1ZYwjLiLSqHSJ0k7pFmzEqXR0q9NYTicbmOAPtP5g0HIMp2HbfXAYGM9k5pIrxgyZEvWsKskNBpePfQM3OYiCAEv0NC8A47yqKvv0Ub3npXvcPjUZKW7IGpmF5TFpWfzaFsLKKohSXrMftzkXXk2gitcKXhQu5JXXvtZEe6JfJpckxjF+7ixvS47jg8LaHZuoZPGEEOd/k44uNo/9hHadbCwmUDgqgfvj4/QDEJadStnsXhq5T3UToz6XphB0qvT2VpFaHOTXxdqqyjiX1tD/gdv/36p90FttI2U/IXhU1vbl3whSi8ek+im607UnZvnoxx8db85YpVjR7uVHF9Pc/o3tOHQMTxrHuwS+JGZyGJEt4uyWRPtKKEo9E64I4W6QmDr74KLKOq2H3BA31sr+SdeGJmIEA+ecdhyet7Sp252z4kQIpibgTz8TQdb7PzWdx7/5c1LMbpwxpXrcjfu4POAyDRR8/w4iTLsXtiWPuK3ejhbPRvc09Ke4Y61T0pTQXbOud2ZWI5iUp+XpMoSNMHcPUQegIYWAKg2Ih06XnNDzuOPx5y/n8g5X0YkNj3737b7YDdV0GEpvejW5pMdH+6Ni17na7CYVC3HvvvbgDGcTW9EKO0ZhwSvMLXGpCEv26ppK3MdKhyqhANDNKdc1olnId0YOoQrRroADIptRhsUWhW0NV7WFoBjJSuzEriqwgoyMkJ56YtlOQy+usc6NnUjxTe7ZdIfeBX0ookzX6n9W2JH35lhIoKMDd4pw8qXcaJ/VO4+5lr/JY3R9Z6jqWjw8/vd3jsQ5KRlI6qLoYZdAdRyE0A73egxAxCC9ZSnixF29M6zggSZJQVSfghPbylLKzCRetwFWj4xgzFPlXFlMNrl+FUemCTijOatGvX/HuPYBYRAy8QWubvdYnkHfrfARSY4Xd+qmh4q51Ptb/LRsRYnDw2SNPoskmkiTRXZLw15VQ5xRMnG4pVgushw8kCSEM67dp6phCI6xp+JQUJNkyivWqEGWvrifg3EFp6pdIQ2VKlu5BLDMIBfzEp6aTkFqH5MluOA4jmkgwoP9TZGUdzuu7V3HZdokny3vw2px5OCUDKyzfCmS23je+ypJAN2UgnVDVGiKhITg7GCYaNWoUo0ZZYpSGYRIJ6Vbl7IhOOBgiEgyhhaxXIUlI7fxmha4zdUMu3w3sTpG/mrduuq7ZcsU5BBKOwYVJvqQQcl1HtwvuplvvKW1XhP0fxO6F/UmL66ifEEoLl4I/Klj16sZXScpLQpEU+q9KZdiO7IZ11qi5De/lsETC5IGwChJrkmGRFRMREgXUJOkoLg81hYXR3SutUpBD5VZarLNLGs6u1rhomaKw2Blg+utDcCLhQmJaynCuPPltvFU1DEhy0C2qJnza3NUAZObnMe+L7zk1uyvTxlgKr2Wqn0Qg4c7nybnzeUygC5A36lZKvM1TM+uHuFt62CUEQvcyatTVtEVJoIKzluTBNoBaoD9qry500QfhV71IQjA5rozU0u3EFW2Cok3kJKRARjZqOzE8hYWFDQrKqb5kelQns6emCyLez+X3HIfL3fpzIjq2rnRUGRhQmsQamaZhxYtECeuhDosHCiGQTStgr12M6JBNO+hhazitpe5Mc6zigO3hj1Ycdqnt1/YRhqD9iFIIRR+HPe1kCKWY+cAoYjo4lnokU6aDGN0GFFUBVWmW+RSMkwgDUhsxYJ3GqI+r+PXbEBFLFVjuTFXoaLCv6uqgtlIU2a0yq28R3pUppPVQycxIQQ/pmLrlzSE6HIhJ9FU0WBySKdB1idLqXKSYGBxRYUhhChTSUJ0+wtpOogMe0VfLuJMkBw5FRZZcECrADP3SIDoW3FCOXhYkZ+ptVhvLuyETRAuHcXgFBlWkdC/FW9cY31VcbA11q9HSFCd0H8FX8i88s2U+cdSgqol4vX2sYTohGg+nyd/+ujKGsZgM94f8PPMHjj71s732357NeXx8zz8QZgjQaRmvU09tqB2DUZKQIhGOyNAoGlGA7BAgC3R3JoeNepVgqYOinXVUlwbo2i8R9+SNrS+C/+PYRsr+osV5F6yxjBHR4htYWLgQgG/yvmmY99ye25qtc3FoIt85VjFVG4YHJ6xqvbuIEeSVW25pNu+Xz/JY9GU1CdXbGbru5ahSk4wDcCY1VrwVukJyrczpSYdQVVdL0lc7SAstIf+9iYRLIjh7NF4cr3eaPBuRyTdgdZdubM4tZFfBdxiEeeskDWmKhye95xOqLEOYJpIQ1G3KhCpYuGI+XWPiUB0OygqtJ6VQsJSaSgVFcaCoDkxKUZzVhCNlKLILWXYiSc4GQ6tOs/rxmsQ8JqekYiKj9/aQN24E5eFYnv9yE2lTRnLX+Gspr6xm/vyl/LJuC+5Iebvpoe+++27D++zyRIYZ3RmRACt6J7RpoABoVbVADEoHnhSDFkaKYTYzUiNGCGcHw3a6rllZNR08YQldNFRIbbOdYWuoS+kgfVgIDUlq3xDy65aB6e3ISNFNpA50VEJ6VMivnXZcP/YuPpr3bSs117aQDBVJ3fvQR5tE20EH3qm9Ei0Uya8UOZu5Jp/VigtHD5XYr2c1SPY7ZRk1KuGvKjJKXQ3dAzWUFm1HivWwaftKjAVfI2MFf9eEa8juNYgBvRrLQLz7xTfULY+nSvbT++Qe9Bv4a+q6HMGRLeY8+q/3UPwSi8+4b6+ffvnF+3nJtYvwZ8+ArGIWuxE0Fr8zkwoYHHc3WWMubJj33cyzkdXihr8j2jsAxETVrQHGdjsCZ+nLVFQsILvrdfTu3Ty4viXhgJ+d62aRW/g5QrQt8dCSwu25CLOGniNPIC4lCdXpwuF04nC5UF0uJFPily/zSW1HqDOi6xiyzIaaeHypZexYN4FQ0LrWrl1tSXpcfPHFHB4t52DTGttI2W9YTxr1BCutm2tmi2KKwxmOp8DD7X+6HQMD3dTRTZ2AafDnZT+hFsbx4NhDiQmlcEG+hioEN6SUMygmCV3X+XTFC+SF69CDdWQllXBe9sk4ZR95u1xkZAgUqQJz1ixUPYh20mUIXUN3OkibMq6hDXF1goTufbjw1PdZ8M3LJK97krpMF3q4GleKk7gpRzWse/sRI7k92vK/vPcFn6d0435FweX/kVitEHfCII4866Zmx7jlGks9ctUrGquoaLasLHIV1auazHNZPbdgwVgWMIE3uQIJS4pcaXiqiWdkQgoTM5sMw/SAvJogz3+5iTdmbGHRwiXcF3mBU411bBFXE5S8vPvw36hRPOS405FccQ0ZMm6/v2Ez3cxGsST3eoOnX1pFpTBJ9TqQVRnZISOrMrErCkDti//9J9iRnEJ16iBUpwPZ4cDhcKA4nGiKzObCPFJ25uBwOqjw68S4JWpry3E43eRFaqnq4EE6FLGMg448Kc6wkxipA6XYqJHSUhyvKZIUQmpXhs3SUQHwqB0Ml+kCyd3+Pho9Ke0fcBgVV6c8KQryr7ySiWg5g/+XF8TQEULp0HPUEX8pLqFqSAZOwySitPPdmXDNZ88R57eyoujdFRbnsWjxS81W2wB84TAxVAnhkDlcncoQT3/MtGRifxJsm7/W8mg0nSQJScEKFJOjmUeybCUqRdeRoplIRIXbYislIlKE1SuW4FSd9Bs0pF3PpCNlD2qtjmvd7dH+OgSJBxmb9Bn5VbPYY75EUflndNfObSglUFdXg6KECQQqG7wnsnQqCQmZzbbtdFjDjbm7nqNnzxuQOxgqdXljGDj2NB57/27WR3Zxx8sjUYWMImRS/dn4wilUxezBq8VT5y0BRccMG7gGh7n0ojNJ7prWapt1RVUs+jqC6mzb6FFVlZDsZEN1Fp//fAkpUh0nujY1LPd6vXhblgKxaYZtpBwgwv6oy7yFLkIoFMKFiy6xrev8KN4NLM/qyYPV2/jF70PzxvDl0ETGpTYqF24v+4jphSshDtYDKY413HTWlzSNFildN50y4JBH/95yFwBUJSgQY7kvxc5cAAa89x1x6d3aXL+ef19wGv+Ovv/Da7exXIUHMoYxb/ETDO5zAikplvjc2f8axqrty3lw2ZNc2WsSYzKmoGs6dVV+YhLutFJLzQimqRPWNyK7SvAldqWwJBlVN7ksJYguDGsyTFS5iokZU1q1JzPWxZhRGRRXhxhau5bRwXUATGIR33EUDlVhrZpG2HQgB4J4JEHTb2Oo3oNEfBR5ZRL8BhEhKMutQTYElQaopkAxQDUEIbUP7lAF5W9+jZP6ZMkWDOtNIOhn1q1/apj1c5dh/Hl5tC5J+r9AmGTMWQrCQIpWoJYsHwym5CT5zGuRdZOnvvwBRQgUq2Sf9V6CxJ4eJq74mePOeteqfCwroEarICsq4YiGXCHYtq2Eih+6EZLiqEocAGmpltGlyIRC3ZAkkxVzvrO0NBQFWZFRFBVFhKmrmUNcQGNL7maC1RFcqhO36sLldOF2OPGoLtwBP8LpRRhmm5kP9UaKtw39iXoiQsW1F0+KaWpIptqZpJi2qY/C/H8M90iGDijteub2hiLgL6aHfxzdH9MwMHSdsKYT1nV0TUPTDeoiGtODdcRNPJZTx4ylQq+mLsnT8CAjyTLVJcXkbl6LS9fRwiEm5Iwh3Z1F2DTQK3VERaOSsRSdZKnJe+j0MZwkRx+uPrE0VlePn8+oaa1/gwB4QakFcUcFGDpiwXyYBTfnaHyeMJVDQkOZ6H6fuvn9kTUvqeIkVLkWX3wpixY3Fik1xZfM/PFrDOHAMB0YwoEim0TD2Zj102EcN2XpXtuej0y6081g50g0UyNiRui/+tLmTfYXIYyneH+CSk5WHc74tr2G9UZ/m8KGeYvptsPN5y4naqyEke7mhn5ezj/+bJT/8no7vyW2kbK/aO5IIVzXtpESDofb/VZW6FZq6s/+eITk5LiYfMalNk81PGvqk7z1+lB2RX8zE/ue2mo7ZV+vAGDHuEF0f/kVnIc0D250h0ykGMu6986yUkiXvnQfwuUkIpnExCTg8MYy9PTLiYtvO4W3GCvy/8YdUUG4LW+QZApGqPE4JZVyoVEeU0tKNw99h7atlNsSufozkgw/tw7tnIiUBPwteSlmTDXpO2YBsHbyawwfewJj3dbxPTvnG7ZJmUjC5IzYXB4b0zpIsz5pvA+wc1cR/9pZxDcj+zIqPoaX1ryER/XwQH5P6qQsvrnmIYZ+dQmbT/8MX2pftIiGFolg6DpHVFVSF+NFMw00TSOSu5S+SV5O8ZWgmwa5wXKW6bUk+rKImIKIEOjR13WRVGqFh65Fa3AlJOJ0KQ2l8nQBBhKGgLVJTuoGZXPMxpVgGNFyAY2vwjTQdI10EULasY687DMp6pKA2BlGaqh4bNWRKljctBcEoHFE3JtMiPkagDFc1W7f/90JBID7wBAKlt/LgSkU3ux2Mk9mXwhOD942hscqQlVcsXwu5aIbgdqfeeLjC1ldPglFthRFVRmu3/EqUlih9uw4Mo3bMBeYrN75HbLD06APUy9O1nAnrteMkaKeAaHj21UNJCJ1IqumLYRhQNFamhct3DdMGsMQ6o1Ch8tF0wGEUr8fxTSJzepF2qgxtH6mB/oBR57b8OfuW38GAb5zu5E6snXGWUMWUjRFXQiBaQiEYYJR/15EC0taWUhCWBlDmq7hV6qtbK9Xc9Ha0IGqxzAjqCKqJaIoSIrKkdTAkhrcwIYB8WzsfgPDcsNkKSUU9/iIbsYYlPhLMc0IpqlRVfs+qlxOvnYuTtWDLkLWg4wehnAtvWPm4KC8U/2tSzAyuTsPTn3B6n/D5Ln5nxGpiSr0Sm4cRjrj19WQbfTjL8fXEeNpO+ZEiyoFO5xteAQrc3lg20xMp4wrQeLwHju5aOtTMG19p9ppY2EbKQeISLRSqjOuuYWuhbV2x/L7uyNsCTnZOmEksY72XYT1BspZMb05ZGDrm273f/+L4n89SKSoFi13czMjRQiBOySQ46zId+myc+C2J4n9ch6qDg7DmgB+Wr+C/qdcgupyo3q8qC4PDrcX1ePlo7x4RGQD5s1L+HTBPbxWthyXgKCpUSvCRLQAYw2NvpHOX9w/91vj2BO+noNHmHgxmejz8pdJLUfMLYpKcxk37xZCshO3GaHQnc7gI09tFtPxUuQR/GYZV3n/TVkkyILCNWTGpJDta9trFIkWQvRGn4SeXf0spuSlurvldl8s+xjhEhSue5Pa3hOITR+A7HKS3K0Hg4cPbb6xI9tud1uctGIbK2v9zPzTNR2ud+47X6DFxtD3x70HBQLk/ekdkkJFnPfK+VFFX4Ee0RBCs9JvDR1d1zF1DcMw8L8SoErujeOiR5CUeMJ6hLAWIaJFKyHrETQ9TN2mcnwhjeQk1ap6bESsAFNDY1l8D3wiwGVSKj5Pcw/G1spcXt65ioVaT4bJWxiqraVnwiZ+yp+MbkoYpoSmwxDnRnDC9g3W70ASMkoBKHIEWVjlEyQhRb0EUsM8ORriKTcYZNkocsWvjkmJ/PQZrqrpGFIC7fuEGqkrzWXX6lfRpRqeqzuJGXEpGE4JpWXQWgu27bK0NpyevQfL1hNzRS8Cr+RQ+uHGNo0USaovLthk3/vgUEqJKqRuJQejDRHDevRQNYpo8oQmTDKR2RMdrnVsrubQ0DpOLLIK1U6vO48qd5ATx5+Oy2cpVc9amQ1VNzJt7BV0SWqtGzJ7jnVt0KOy9h1hCBNHk/FBWZEZPa0Lv9QLbIsQzsBOANyl5TgNVys9q8Zjq4/xar68y9zVJFckcdnsMuKcTq6PF6QfMQ6mntRh22xaYxsp+4mAYVK4rICSnHKEKlNVbI1LvvbTTzgTEnA6HVTXaNRV+6l0VvDysu+Ic3qJdXqIc3mIc3l5tW934t0xODq4kgSDlSQYJhNienDnmV+2uU7ssaeiemVyrrwFOTa+2TLDX4cMqLGWkTLy9Kvg9KsQQjD548kYwmDWGbNYMX40fWasx5jxdwyg5Yhsl9FVqOmQkNiTK056M/ps3qQ/cr/D++a51Ma0L6bWkmE121nj64MvUEtQVtjo8bE2ABU/zkNXHaSrMnEOFa/DQaxikFS1hgxgxxkfMnjwUWS0sU1TaCgoODD5SRvMT5sFLnaTMzGjzQtTKJqG5I5eCN849g22lGzkxuhD3CHpGayL7cMxedMhb3rD5wrd6fCPra2211kiwkTey40MIFRZSa03htVr1jN8WMf6Di1pUP/1uKAd/YwCl4lmJpLQ06qX0q6pPK69BfBtNCMstetu3tm2B6cMDgkcksQr+eUs1XoRg583R01m3tLtGJHtTP9r00KKJrvue4IskUefvAALhz3BY9XH4KtbxvN/eq6hgm5HCCGY+Y9T2Cz14+xdKym69Onok76MpKjR18Ynf0f3LBL+8nDrDQWsGBFx8cy97nPHwufJDT1OvTUzPd5Ser5VcXJUn471SgpLrCDS7MzO62Yk9c6kMG4lrmo31TuLiO/Vegj5t0FiSe5yXnz9S0sYEAMDq7imiUFAbESRoObe4RiyB2oGctvyID0LVgPwcd/JZEgV1KopfF1xJ5VF1jHqJwcajJRd5UF6KaCbrY2hYDRWC1Nh8adnkd3tApBlZEkBSUGSZEstWZJBVuhW1YdqX/NA63Gnjkfoe1j4yXv0H30YXd+yLJacwdkoIgddMyzPkrAy10zT8jTVFFsVsc38PPSyMoQpUVsYQjYMUiqs7yyIhFBjoUuLhxSbTmEbKfuJ9zIUhlUaJJWHUQ2TsqhqZN2uHYhdEoqhowqTOGLJi9nFMxtv7nB7QiioukxsRMdtyrhMGacpo8fUUeNQuXzjGrbdN5C69ERQXeDwIDu8yKoHVVNIXmnFQVR+MZPaVbuQvDEosTFEonoEalxzDQFJkogYEWKdsXgcHvrP+I6S/G3ooQBGKIQeCmJGQhihEEY4RNWWp+nmUmhPiUBEgy8lZe8y4PVcsf0zyncGuOa5rwB4YfFyHq7SeFmJrR+NsCYEN+e8wd/yLAlqT0z7lUINoSHjYMbYMeyuLebTPVt4vTq73fXrJb69UQNmVJdRjOoyihujN95xmX1RblqB319JVcUeDCNC/i+v0D/3u04fZ1voomUpyraJNU0W9+7PcRU668vKSWlSM6ktdmvdQIEf//G+pa/jczH25lPbfXKUTA3RQQXlfeGf+W19L/Ec4czlsyNOBcA0IhhNUuCEZlA8/2eyRF7DvMO3zGFL72JkTy2zNszi1JGn7nXfkiQRlLIQYQOtoLyh0i+mNcSBWV/9F8yIwIyswXflHcgt9FRE1EOgZGS1tRsK133H1uJ/ostWPEhc6FCy+l1NTHxvWF8FwL+MCP/aEmFtQhxp3tYVeQE03Xpiz0xtW5emPbxDUnEs0ql4cT3qTU5i0trefmeo/XkNNd9viGYF1ntiJNxGOu4aqIuvRZZkFKEio+CQnMgoJIthjPPvpsIzENkMotdqDQYKwNnb5rIs/u+8Hds4fDh4op+YtEaDrCZQCXFgmq2zuDxODxm9P8T3gnVNrc92bC+a6SH+AnkwO+dDhENi1DlHk5CYzGFnnse4088BSaKgqIrQpk1sxoFkyrz0p3kd980rz7Pt+UYjdjaW1ozkcEBEa6i3ZLPv2EbKfuKFvi56uJ0sPcxKATyc5gXPZuwo5bpXlnDr6X04JVPBMCVqI0HqwkFqIgH8kSD+SAi/FiSgBQnqISJzviWxrgjvsAGEjTBBKYwI5XFZdSlphTr+2HKEO4Bi6qiGjkMYOEyTOM1ERBQqlFSqf/gF+KVVe83N21vNq9VqGb3Izf3Tj0dzSUy+8homjbHUHiORMMue+QY1bMk7HybXgAbhR09GSB78gQlIMTHRLAHQa3Zi6Gdg7sNYvh7WUJ2NTvU/jhvFH6PvNS2CPxSiLhjCHwoRnr+GchKpu/wbemUObnuDgImGKrlJ9SSR6knil9JcpBblCkKhOtYueo+RE65sKFRW70mhtpg7v3wKMs/mLnk7ijIcgJiYRGKixlHu6q9I0qqY+8nfmXxWc0n3zqKZolO16l675iKe/34uD/u6YHbCrOmSFKGowsnuUic6DiIVcQzYuofEAW1LcctmGFP9dYJl9cwI/IGI6mLIpJlopkATBmHDCkyOmCY9YqzfSHWghohWhbPJZapiZxXu2V81G5b4mT7IpqX30ziMs3cMTyqK6Sd77poO16t68iYKX5qBvmsjzkEt4qeiKcz+2giKUR9kbJ3jqqqQl/sKekw17mAv4uRDGHLcow3n1tqxlRy2ZBP+qD/qnZ1LuH7ARFxqa0+QHtXhce9jFlLPUw6jOGsLxgcF7HhlHof8s+M03Y6o/XETZqQLQq9GEvWqKCC0Ug4pSuK6ez7s1HZ2fDAL5i6nOElGP/wIkn9YR21sdsNyJXUj/SY2r8M1qFsXqAFPO9owg7JGs3HAOnybqwBwXzWE+ESnJSxn6oio+KMwDRZ8PBdHnUpSWQyp4UR2btnMyHHWkLcU/W66PWb9Ts0v78NVlcfRlw5ElgWOSCmSYtV2QpWRBBhbNvBUapg+2+LpGcgg0zOI2nAZckIm/Y7qiYhEiJ00sc122+wd20jZT6Q6Vc7LaP8ppjwYASR6pqUwomvHT0umaTLr5vvxzquhNrUP0154r9U6351xJGaVzLQnfm42XwjBJ1ccRfruOsavWYhZXY1RU2NN1TXoVZUU3XwLJLd+yh0aP5geJXXoTvDUQN7OTRA1UkpLisgqTaPcXUNxUhX55hF0i/wCRhgj4iUYOAQpGEZ1lIGQMYwRhMyjMTfm4kkvb3DrdoQeNlBc1ilbnFNIYpdknFFhLofDSYLDSUKclcK5xyljBp1kde94yKPCXQfUMfPzIxCmSUHiSIR8Jad88R0e4J7Rw+j/6kDGAMy7mUXjvwU5hkfXf8tJXfsxNimblzPPBmBAUtvfW/aYC2D1U0ze8DLrRpzD0D6j2lyvw2MXAqUTmRcut4v0aI0Ul3fv8QtnPNgYhLzlw5/48Sezdc2CJsgi0qwy8a9BQiMWL3GuhA7Xmz1vMt1dVZSGGp+oE3vGsz18NIa5Cp+5lt3zknG4TG5YY/DCNJn3Nr7HySP3LpMOVgkApQNNmYb2OqyhmB2nX0LmPy8n7qLGlPrN28sYDox4Yh5ak8upSwnzryPvJT6mlqTI0YyY9hItSfMmsn3SYWyvzuOm9av4fGmAuievQHd7QVZAlhGyjJAVTH8tKdCqxERnSB/en03vb0HW9y2jJLB6G6HN+UiqDIqMXhYAZxU9/t08zm39oENQjzyb/J/XWnL7ikTy0F5UbMzFiBhWpWpVJuWQ3iguhxXQDaRXmPDNfADcFe/y4fhyutT2ZGyfpWza/CTrN6i4nGczceJ9SJaLtEP9nkGXDuXbB37hkFqT3NfWM+qB8W2uN/XPFwGQs3Mrxa9sZebKFXy7fi6BygCSKVliiDIgQ8AM0D+hN/3HZcBDWRCqanObTyjAAIBNrKncyKKivlz1+APEJe+b58umNbaRsp+QhUAzzVaKr/Ws3lUJwONf/MxD4RpcqolLFbhVgSxqyfDG8NhlVyJJEoU5e8j65gN2x6YiTWgsklVVXcK25T+gKg4c1QFCXVsbRZIkoQYi6F4XsqoiJyejJjcaCDUVVhEsR2xzvYZvZ7/LwLmW0qXonQybysldspRHd12FrKj0Cw5gBCOQTkpl6qHTgAsAK7JBW1YEn20j8fxheIdaMSiVW1ZR92YV8qJsisy5ZJ125l77UI8YOFxuKgpLefcfV4LkxhWTgTAFSUohQ4Z2Jfuo04gfMgXJCGPuQ16qECZKbBl99U0MMVdTGkpjZ5eufLVpC00H3oZWz2Nb4gm8VtWD5TXr+X7iAP4qtvGk1Jed1ZU4Nv7CkYOaZ0spIZNVSccxomImX3/1NnmnZjGtd+djcaChbl6nqB+SuvqLWQQkiVPj3Jw9+QhiYzuWUTej6bjtSeYDJJsbqDY6EyLawX4wUPZy6TFNk3hXFUX6SUwc/ceG+QYGX+9+EYjl+D1pSFUyCYQ4ohg+Hg8bHRt54OUP+MNpx9M1NaFhW7t3WPEBLrcDp9uBy+1EM8y9BqwC+K68Hbl7L/bc8hjaD48T3P4wOgqGpNBHGCDB/Yf1AlRMU7CqdBcf73Shh08hKyaZ1H5Ht7ttSZLom5DFV0dm8c6uuyipq0GP96F36wWmGc3MMnDUVQGtK413logSxh30UrWrgISsrp36TOmLS1ES6j1qAtnXFxEubH0MwqS4UGfl+2VN5ha1Wm9g8iqmPHAeQm89ZPPRuKUU+STOHX4ypw76I9u3f0dJ6Rfo+tfAfVSE80gHjv30RCI4oiHQMhIKIFtB0pKClCHzVe2tdDFg4fyfOHz8pHaPT4rA187lmCUmAU8tqldF8SpRmVor9iSuJo70umgF5E5Wfh525tUMG/+3Tq1rs3dsI2U/cdqPnxEAblUd6KpVybdX0MsQxxAMVWFKQGMcHoLFHn70+XH4dEIaBDWdVUVWsbz7QyE8Hg+BSitYL/nuuzn0xEZtggV3/pHe328EoBuwY0hCm21xBCIYLQJm6wnUWkJqziZGyvrNS9j08od4gapEk6knnsNP/jehNkhwRwGSCYMTTwMFfEmtDSMRzeCRXI2nW2L/Efju1dhzz5xOq0DrYRNPnAOvL3qzFSHCdVbd+WN6riSt0g+fvk3Bl/3xBXPwu6yLcfXzdxBaX+/Sj+4sOp6+rqQ/Rx42gFF/faJhP6Me+5KEsjjGdIEn1ESeSfmC3WWnIQScE/M6Z4jXeIc/sNawyqg7hKU490+tJxTDSzPP5pSa7ykjgd1Dr2fEuvupzxVaWt6LD6evZ+3YHsQ6VS4e0pX4dlRsmx17Jz0pAI6o5sdPXa04iaXA69/OZ8HZHadu7wjq/Dte4/GXVjbXzmiYJDZ5dBJDK9m+5hf6DGu7Ls/eMDGR9+KNqQlZMUsu/Wd+XuPm/KMfso7N6WLK3+9jzqN3EKl24GoyXFgWPaW/Fy9R99wOeo04kstPmsDiH9ez6vOyVvuAyciOXXttr+Ty4Dz2IrjlMVbGH0PsgP5WxpJpgKmhJmRyzimNQZHbPluNkhPDyHGX0adL27EqbXHuWbfx9PQzGD7MybGXNI9J++bfj7Bl0c/txgrtDc/gFLzrVepe2EGevJCMc0bhSEpCViRr2EKRUVTL4yErErKiIDk8RHbOIePBy5EMA6EZODKHt+4fYTAgqZiRZyYgTJMfP9pNyGF9GSec6EFSVGZ/uptN5en0n7+uQUAvfNWVuF5+hfy+Xbnp6L8zadAUnKp1XiQn9+f7WWtR1XnMnnMD8cZyUGBk8onopoJuWpXfdcPAjAbpGtFaXjf1/ZrHtp1EjxkK5hGtK8039InpAQnOPPYUDjns0DbXmT59OsXFUeXbW3djRiIU3jsHoceAZIKQCW+dSWTrDLI/fA/PkPaHlm1+HbaRsp/pMmIUkXAIKSefQrOQ7l3HIBsC2avgrIDDAzCgp4Ox51kuyfnz72LmjkW8t/ks5m3Yw9SRfQgWl+IA3DEtxq4rq8jtqtL3kX+jaxGOaucm4gxomOltDwXkFmwmHvjh58/oMu5oQpEgW3c2jtmfe9M9DO4zmvGjmlejzbn5R/K6FjPrrdkAJCYmct1116GqKiJiPYFILbQEFIcDSUjIi9NZOPtlFld8iSyDpFjebutVapj8ZSaeWBN3jJe03hMo2TGfrGHHUpKzFZe6hQpXNhE9iGwECarJ6L2PBaDoxU8wIxLOhPqbfL0+BJjZvQiVGxj+CEUfrSHoCpBUZnmWHlsV5LP4Ag7LT2CheI1eUi2+3aup6PUNXShgpnQiz2ycReWaxRw+YDcLPVZ/r0zqxyk135NCFSnr7m843mVZV5BXO4Kagjpemr4ZgC1ldTx73N4vbIboXEwKwDnHTKDXmg18u3Ebr2T2AaCuE0/gIaeBJsElvVJxOR1WaRdTYAqBEX39rPISzgi9RfwX58Owvd/g28KUTOS9FNtxO9x8vv0sRqf/govmAl0jRo1gxEff8OmHMxh8903MP7Q3zxyT2yA2EpJTkX1p5K/6iZs2bSJTT8RFGoeemWqlWIcNtIhO7uw9eLTOeSbqi3SKnhMZd97lHa67MCfMkLRgmwbKxsUfUrpnK1I0+0SSreJ0kqQ01O1Z/90OjrlIQ5Yb+8jQ/3+Bl/0unEzVzgLy3l+MryaJ8Ae7CbO7w8/IMakILcCCP17JMT+0H/gtAXGpsXQ72tJsuviIIdTmFuFO8eFNtx5aDiurZe4v8N2bO8jIn08vwPXyK9T06c2kjz9F9bQOoB8+7B8sX5GPqn6PW7WO/7kTb8bZiWD7vH/MY62Sx/L7dhDfI82KhVNkcvJzo1XbpYZg5PjYhIbPhUKFKEoMDof1kGYYRrOioBWvv0Fg6VI8Iy8FER1W7XccXe64Ac+QNtVrbP6f2EbKfsLn8zFixAgmT7aGZ37+cBbzaso44dLGOhtCM8i/YyEZqdbFtra2mEDwI9TI0YDENZ9u5a6KGgYvXoQD8IQDzfYhByPoibH0GXUUHeEM6YRj2jZS5CIrCyEjR+azG29stbxvVts1JhRJZXblxoa/KysrefTRR7n11lsRmvXkJDtbDxN4z/FR+kkhca5Ehk0bgaFr1qTpmLqOYVgVa03DoLaoAqFZ+UJJGV0p2SHYteZ7OOYe4ve8RX766cjjTqUodyuKoqAqClt++piilDQGHtGbtPveYOeyPRQu30lyIlQU+3Esfp3VOwooveEeRqcch7vJoMqkEp1DQhJJwkmxZrJReIhNvojJk56kas5hvKnAA8VpMMKKSekjttC3djf/vOheVj61gZE1Ufn/cU8x/NhLGC1JLDVMSoMa4/41G8kQ5FYEWvVJPS9u+p77ipIxrTquJMudqzeiqipdkhMbDJSj8nO5tOfeXfxm1J39tzP7E5/Utqdt1ZMPQwhSqepUW5oi9Ah7Pr4bM15DC0Y6XNftUHjuqod449trQLQO4gY489xpcO40BkKDrFxE19BNgdfp5KOZ81m1dBGROgkXaYyZMrjZU/VHH/yCQGL70cdEU42jk9dLt8cexdmkZEXuyg2ogLODITMhTL5avpBt5UmcPKiizXV+ePFt9GDH3hDVZVpemt/QSAFI6NWV8F+P59UbbyBJV8m+8kYcDpdVVFA3kaJZTZIhkHSBc88eaor8pOwqbXeb9dlNNd98Q2DpEiSXm6x33yVpcHaz9QZcMImKXZ8TqovATitfPxwTw/D33mvTQFn7zSvk7cpBUcYgKYexsrqC+PgiJk/unFH5kvd7HKYTBKi7LGNMIDCEiROVrt5UVEWhT1x3uvSy/JwVlYtYtcqqH3TUlB1AayMl8YLzMf11SK61SG43vpNOQXbGoiR0nEJu8+uxjZT9iGgiaKQbBkqLKAPdb92ElOiPdsuWT1FVjetP/wvn+b0c98JK7plTBAzDcdIg3LPK8cx8H7cw8KDjSDiLMWIde5ML6lKqw3drqez6F2SXB8ntRnJ7kTweklauRwOkvhnoyU7UtZarU1cEf33rc5yO1j9G0zCRJBmjxZhtOGwdT/1wj+xpfbqlHDKOzS+/SHbsYCgYw+C/nNhuTMTj55xIyc7dPHrniyjb5iPJMfQYO43gqs2QCkl5b/BknkKkZS3hCVOQFT9pwHevNWqVSMLBaVl/BuCz3CcZndJ6OCSpJpWIGWLIXSfg8sVi6hqrvv8DCa4ynjG+IJI1AY/Dx4b10znc+z561ztxKjJ6jJWNsOPUrxkxvDFT4ckZH7EkN4BkWJoVA7s0JmlHDI1JC36g0vSiYhIgFi9B/pRWw9ulKhlKxzf2puwpKQckri/N4/YLT21znWU/reCbeRuQZAlZgu1VEVC74OigkrN3wnXw9Q8ArPj8SQKSlyNO/ANyG56aYMEWalZ9i7/WT9yG10hVKugO7BqbSFVV545DEgbmPlymnKoDJ7B6Qwnb55rEMRYzqGEgWrn9hepA0sIYVVUI07RiQDQNDIP8v/4V95D6IRxBdVIWyUDCgNaiaKZp8MSMT3h1sZOQ4WJM1wJuP+XsNtsnDDjkxCEcdcGDCKFjGjqmaSJMDdM0MA0dhysWpUVdJEPXf5PquDd/Pp1DKguo8MRwQk8fWWkdxUb15quCpcQuns3yZasYNXpE61Xqr2mShFFVjYiU4l/4CwmnNc8ikmWZI2+34s7ev2A9YXdPxj3+GK74to3heSu3UG7GkqrUogmJSmLZE8lAljo33OUwrfNRpApuv+6OTn2m3kABeOcfN1CSs4Ngt97UxejMG/8aBd26EpZ2cfyLn5IW33GJEJvfDttI2U+0DJYNBYKtKiPr0eJhSrTgVG1dIYahkJzUk9QUhedOq+a2LzegOpyc4AwQ75IIRATFYZgRSQNPGkZs8xu8Fvbzxbzb0A0Nl+zAqbroE11W9NpMWjUCQBIs1XrRNfVQpt7Wn7Xr1zF+TP82DRQAPRhGayOV+OabrXF1oVsXMqmJJ6UmfzO71r8OCFwjqillA7IWQ23xGOK7tSW71oi8ZQYoyYh+57A7vSeuzUVsDY1jizuGCE4uyjoceYsTAxMdgw/dvyAlWyUFshJr2FXp4+jJKnJCPK8u+gKHUEjCYOae1+jjG0kfX/OLcex5Wbh8VtptxfYNVLkWANBDhBmQ1Jv45GyCG62YCUm2bi4DzrmfnKKr6D2gcVuRSB3PLfQBPrypYWrVBG4Y3TgkUKf52WlaHo+LfTsJmYKhcT6uHDCN6T9PJ17pXKXf2ppaZu/Mg/QsVmhtZ6/MnL6Ap2esZauvGym6HxMJgcoQvRSXu/2nwv6HTmbZ3CmMrpvDoWvvBmBTySr6Hvs31GwrdipQtJO6F08g0SwmXbYMV12SKHANoWt4PWGXgiM5s509WJimwdJvzqSrZxPbqrMYd+t3yBL8ZUw2Z586sM3PlFYEef25lQgTjFqNhDqT6mQV4pzoSa2f1uVYHw63Qv/PljXu1+9ny6jRhNZvILR+gzVTceJN6oEUk8qu/ABpedXRIUgZE41rPniVVSV9mNKznKMGZHDO4X9oKIrXEiMig5CiBpMTRemcZ6Bo+9ZGg+D/wUUTj+TLimK6rF7IW7f+haTjTuWKk0/B7Wq7HV3Gjoa3X2fr9K/bNFIkWWbgZkuYsvrrbyj4+9+hgyrdAH1PGMyMbU4mp7ZvIBlIHNnN4OgrH6fIX8Qxnx6zD0cJiYckUrm2EqOi8xIHTmcqkYjlNSrJsTwpeTFZdFVqmHuUFfvnDGWzZ94c0k++aJ/aY/PrsY2U/UhTT8rO4l3N/gaI1FlGihrjpbi4gNqa71DUGEspEZg2diD3zthMnySZ+/5yfsPn3lz2CzM+qyLOV8qEsb1BCHRTR1UcLFz3FvcVzkEVAr3eULpV5ekBlzFp9A0QDmH6qxGBWkSgltVzvmB4zvOsV3ry/U6FN3ZuBzz8a10eLnMbSDJ/HxPPFWc0ZhXpwTBOVC4adjS9T2st9d4YONtopORv+oQS5UtckS6IDBNDCmK4qshUL4A2tGFNw+DxK+/heiXAn0aP4eU31mHurMM9rxRTgvfdVgXk9BgD1xZrKMuBwh7Z6tMuA6zihl6HjmxE6HfmRAp2l+GanY7mqkSPv54dfElhzDa+S9uEGo7lkurj8OKk8oNdlL+7CVMVxEvJpPY4j4rs76hzf8LaJbWMP+E50jMugcrV5O95jRf3/MjAXmcwsUWNoaMefQ/IxClH6DZaxSPXkNmkLEKC00cCVVSRwMMjT2tm2OpCwtHJJ+mnv5vDi+mW8bOoaxa6rqM2kX2vKqvimoXVkJjF0UYxrz55Wae2W8/om75ACMFbn6znog3jGVj4Abz5AfpV6/B/+ypq3sekyYWUKl0RQ84mdtTpeLsPoWt9hdo5vdGc85nzw0AkoVoFAoVivReO6KtCKDYHRzATOXgRJ/VIYububXywPI9tpesa2uJ2u/lo84tlAAAvmklEQVTzedNwqArLVxUTmx+m2m1V6q1OdXDbfW2noYI1ytEyplKOiaH/yhWYoVBDcPWev31AXKoVNzRyDRhr1jYzya9zp/O3AXdy+fg7OaLXtL3239oZ6yjOOY8L7/pgr+vWI4T13T99qZXhJ0kynvgu/OHxu5D3oVjd0T26cfQNf2Z7wVm888zjhD99iw/cHv5w0oltrj/2qEnM65pJzJa11Hz8LLKikh92QVpfeh8xqtlQTX1AbPn6dQS8HuRoFXDF4UByOJCdTmSnk1CllcnYUTFD3RSoYWvouUtMFybiZR4B3vr2ai45oXU6d0tqayzdHPYhA3j8kYspL1/I9H+vxpXQP9q+Vc3WibjdGF36d36jNv9vbCNlP1EcLubTDZ/yRtEb+DU/fehDCs21SHS/JbGsxsRRVVuG21OLFsnk7dmrmLEmH4ciUaXJxDTxSBi6xtayXCCBqUNjiAvtZNDLxyIpfg5RgqyXrbHs74//kJS0QWimFfPhdUUFuTxeFI+XesNg/ZxZjI4x8IdaPwmGZesJ+/5lAT5f/R5OGVyKhKob9KQL58ptj9eLhtTWxjuCYQZw6mkceYKl47LmlxcoCz+G4mg7ViYYCmIqCh5VJS7WxY1/srRGlu+swO/XmPuZJUhXEFBRr+6H/tJWNHTWK7uJFW56TZoEQGxqDKJIwQyHCdREiKnLJoBMecYvxJKKhsnshG8xMOmZdzxDPCZuOUIwUkp3h3VxSso7lsFX3M2PPw5G1quJhP0cPeJk3vxmBj3ifgR2oJfO4ePLJ+OS4pFUFRSVKXsqSfXN5oS/XcHVtSGWatmctOBLbu/bh7HpQ3h24zdUYaV8tryAbza7szkIH6zfjCrLOGQZVZZwNLyX6REfR3ZiAslRT9zTBVsYPag/NXm7kVUFAxlTdVJZYt0kHhskc/qFl7bZ3x1hmoKz753N8lCY53iOT3vkkFXyKPpXDxFf/B5hBrKn6+F0u+IVqwpzCxI3nYGIjxDfo5dVQE5o1nCHiFhDIEJDiAix+iD6HnYzk5Msr0vo7p8wRYSaAiveQxImGhpPvGOSlBhPab4fr0PjgpuOIivT12q/LTH8AUyj9ZO27PEgN6mRI8cmYkbyqR7Xg1Bq16gSrYlpQHDdGuJrYhHA3xfei3vx/VZyrBSdsIbS6t/37eElscRDQX7lPvV5as+jKN65i/h0L8I0qS7Op6pgOeGAhid239OStfz5+HZawdsZSe0rMgMEkhLpt3Yd+Ws3NJv/w+mXcvyDtzT87Y/GyM1ePJfaNQvb37cvCbr1wuzAM2SgoJQ1xrjdf9IH/PGr03msdCFvvz4kmngMRMtiWlloVl87Iy4GF1rGolqs8tA9TwASwggjjFoUVUaO/r5Uh4P0nr2RZblhqotxU524lfI0Fxm1ETKSenLFtRdiGAaSJOHYR0E9m/8ftpGyn1ievpxiuRh0QIJ4dzyZZnqzdfLKt/PMkJ4Eti0nzu1DVy7C6QmzaGEOIb+DJIeOT4Xx/bsQCvrZ9PSZjAgu5G6h8BdXHOpyHR2V3mZffjIPY0FcgAuzdzI2eypp6ZaomUtxgdK+O/+09FIogct6VRERfjTDJGwIakImn5d3Z1paLUU1JmEDAjpURaBUxPELEcY7M2hLOq3eSGmKYQaQRZOnMBGV41farrviD1gpqR6Hg807V1Gx9DXUlH6MOepPFATCXOebgFJTxWliLYuLdnHYyBQ+Xj+dGjlIshHLD1f8laNeehxfqhchG7z4t8UEnaXUpVmuaoFgU8ImInKEsBKmT3U/wgJWBAz++PzxLPpsNp98+hhZA4/htNuvtQSuTBkzdhFzZkzluNN/oV/1coLR5kvlMvE7clEjAtkwUEyDYwyN5EA1+ox3eerau5i6poJlWjanbNTJ2PQDhcIyUHrJBcDwNvvhr6Whdr871Shm56ThLI7qpMwqLWPjV5saln8WHkptkz6Pi/Psc0rrOffOZksgTFVUBK2UBNSufaAE3MXvETAmUqH9jeqcKpxrdpA2ol+zz5umSWreiQQGafSZ0HGAd0sSJYn+GQOZdrUV27B+Rz4fvvMawV2ryY8mGkWSBKEtPan0J6N63agxbuvVqVqVkZsgJBmzsq3U5BZIMrJLZ+jZh7da9GPecoTfyR/7HU1JoBhTGBjCtOrWCANDiOh7awpONAkviJCR7+aje56yKjJHPTYS9XLzMoYeJuwvxBNn3RCLc5w4vEdwycNWVt3ct79k5YxX+emdz3C6HXQf1Jd+Y4d1eBhCCCKRMvRIkNWzvkQ44cvJ5/O1Uc2gumJ6xKa3+blBj/yDri8eRaG3H13qtoGAHTPSSfnmY76VZRRFQVYVnBvXkAaMPfYkYvv2xdR1zIiGqWkIXbP+1nXW5uWxXYDUwbCijoratfF4EpJ68eCUp5m1+hWMaB+L6GQirNdoX8cs+Qt1sTmE3aV0TemBGV2vfM8OhKjFm5aGEAItHMZfUcYsXypOw2R0uY4pa8iqH1dGAiE9QjjGQ8zAbBRFaRZAa7P/sI2U/URMYgzHJxzPCZVH8qeafxKJq+PEac1dwxukDL7t5qC/lMKesEpIHkPIUKhRBWldZRb/ybqolxTksf7psxgVXEgRKdSoidQ6Eik344iLlHK8sozjlWV8Hnctp5+798JnTUnAcrH++YqrmvnCd+du5/MXt3DuocmMn9h8fHjt8gJO/nQVmwrDxC/eRbK3mJQ4J4rqQladBMrzEQ4D02zULLCMlManVRG96S1eNp6h/T8mvXtz3YK6sHVzVrRKer9zDgOiVVffD8fwyI7e1JUEOcO5BWSrUvG74W3UykG69BnBmMfuxhMKkb9jN2p/BdfMUoKJddQny5iYVDmr2JTYeEPPiM+g+8kKu6cbzF+6gvFnHMXiT5/CH6oiHA7jVj0MGfAyS+ZfT0xSOcGyHHQ1gKc0kf5DHiBhwiSUs1pfhDeMHohZXkzX/FzWpzlYUrGB7x1ehKoiU8ul2UMYmnJCq8/9ed37SHFdOHnaxWiGgWaaaIZpvZomb+wqYrbiYtWeAmb60kitq+aGnpkIsyvCNBGmyTsrHIyPD3FqZgxOh8Lk4w/r3EnR9LsOhBihOhmWnUggYjC2fwoZo5MJVVwHngQCg8+k4qvVJPlTqd5c0MpIMSKaNVyh7ptxBNY5IjU5J4f07sZ9d92BaZqENZ3PX5qLc8FrmB993ExKTCCxeMwdBL3Rm7BlCyDie1Md3xtTN9oN1i56/AuQYqGdbKY91Xs4OjiGM/vdQlry3gv4fb3kB770Posu6RRsXQr1irdC0PArEAJhRgCtIVhWdY0D0RiTlNK9GyCz8SdLbXr19woXP/I8qsOB4lRRnQ5rcjgsCXdgyXePsPDdeQjD6sNgdizbew4CAz7JXcGNQ9o47xa9ws9bXiStfxfeLtiGM9Zgec9rKdy2m8wd60j96gNkYSIJgceIUO2MwXvokfQeN7LdPlj2/HNQUorcwU3fQEFxNveq9syawNVZE9r5hMW8e55mPRBb15PLzh1C6qixDcveu/0Rynbt4k9PPg3Ath8+Yfqrb5E0IoU1MSdw26pF1DjnUXreBnQUMnu+S57ozuWZ+ya8aPPbYhsp+5GUmBQmTTqZx+aZ3LvjIS5aeCkPzriWLt4uyC6FFc44yISLvD3wuVSyfT5GZ3Wn39IfSfbFEKwqYfPC6fRfchujpDBrncNJPe95+vVsobOx7DWY8TdOP6r98fh2Cfut1xZP2JGIdUd3OVu7ltUKy4D499YixtTM4OpD3iKv6QNq9IFo+08gazFIyJiOELHhxnTmASMu5pcfF1MUl09aaQ6p3UZw0/dzWRgyOCvBw8SMVHoEC5AjW3E0KQvvWPQlVfo1HDchC3nVNtDCbPzR0nTwOxMZmRCPJxRi2yU3cPKAnmxbtp3DF99B6Nk7+faLHXzT/RvCamNq7zlZFzK8+2Cm9ZpGVU0t709fzpJvdzJx3GgkJZaSnMW8dsNNXPfqcySr3Sldl0Ts0XtYvPAYzFRBStURJA8+tt3uFQ4Z5bs8ir67HoAewBWTMxn0wg8dfi3OiEamR2dwWtuD7Csraphda3BKjjUU8vjIAQzt1vzJWKz4mr7ZKZxx3r7VERHRGCdJktAxmTwwjSsvaP7U7r7sQesViM1Ko+xfq5CU1jEHRsjKUGorHb09aooref2lV6kzg62GwazKzQpeReGk8yew6McXCCYlkXjNLZihMEYoglYXIJiXTpwzTFK/rhi6yZ7N1nDLyq6z8NcNJy6hncJ+hXHILheeQW27+PPjy6AMNi5aTtqJbcd1NOVfa+/H370KJWsSL1/yVLvrvf2PhyjNWcAfnnyDpIwU3r3jI2qbZDUPnTyaIRO/QtcMvnv+PbYt/oS3/351O1url+UTSIqT8ZdMxeF2cWPAMqLv6lLIFf2OQQjBsd/eSFntZpCsQRQtlAOSi0JV5Yq07jw+7nlGDTsCLmm9l3c+msEdqyDji83cHhZMm9i2QFq94fXQ65+jqGqzoRZZUVBkCROZpdsL2fDEpwyL11Fly7I08grIjtmD7FCRHA4kpxMp+r60KMD6QuuaN2ViFamjpjTbbV35LvRI44VJX/MJXbs7uHH7Y6wd+BKLi30MyxV4L+1BSvpxHN1jKIqy99ISNr8vtpGynzCFiSJZF+ZjJ55KouHm+tx/8rZjBv/IuRRVUtgxxlp+ex1Qp0NZORf+/BVJ4UQO3/4Nnn+/yQgACbYf8zqHHHEGs25/gW8rtqNIOqqso8g6KhKKeT/qa3tQnK+jKgLVAapDImt4Crg/QAiBLLtRFDey4kFRY5DVGGRtOzEulZYRKfXpxE2NlEBExzSh34TuLMqKxy8Jft66mojhYHTWKxhGBKFrGJEQteWbkZ0OcFrqkAiD1P6NwbdOTyIPJVzAerMHfXbl81dpPe+7k5EdOo+iMmvJIpbm/MXar+xGl1VMJCaJ1Uw01/DsMVPJPzSDT2YtJD4hntEDejIoO4ONS9ahSQqRxd+zdVlfdv38HZlA0Y6dBDwxmIQYIgYhFevEx7i4fVLjGHtZNMBPTrFurGf+80G+fvJhtLAVlOfOzsZXkMCmj130HJNPrBShrCgG/+GFxHRpO0Op50cfE8jbhNDClF9jCb2ZOXsfcjBNCaUDufqze2XyzoLVlMYlkhQOMqmNarcCCbUNw2FvHPv+5RTqVgaMeyDMLD+U1NnH4VLc+Nw+xo6a0BCYG66po2DhOisJvI2hJD1qpEiOzhspFfmlVJl+Dkntx6ij2vf+xKd68akKekICPS9urN/jL6mGO1eQnSUz4XrLuJrx/Fpy15YxsmAq4UCYuIR2NioM1KQSki86o83Ft13+IPm3LcAUncsiMWSdU2LP496z/tHuOtWlVZTmLEZxpuKOtcYPTV0gS82HTSVZwuFSmXr1OWT07YseiWBoOkZ0aMUwdIzoEIse0SjMyUPX4hhzwrUAeOZ/ySCxmz8OtEQL/LpOQdlcvLEDSYnJRggTyTeQsemHMn31G+xy78GT3Yf26J8eS2//WnZ5uvPVku3tGilebwJyMIfa8kIkLA+SFPUkSdH3AhW9+gQCZfEsAhQiUb2gVPRlXxPrby3PP2fSc9ZxOYJkn3QigZqIlZwgwOtzUlfRXHzQGHw230gv8GBKD9ZtymPRcRJvprmZGs6jR/dLbQPlIME2UvYThjCa5fiPmXIcPV94knJvBf0ePQEhBJ+GQ9QGAvjDYd59/m1+OTSTx3bebn1AgbWeMYQm3smYsUfSJ/o0UliZQoqvmoxuErou0DWBoQn0Oh1DjUU3JIJhCcMvUR5Mprh6I8nHLAUhUHQwZRBNn067Q0xSHONatL/eSHE6raevqkCEEff+0Ko829OTP8SpaPy040ZAIqhM4KKpe6/8K4DtpqXYuMOXwXVV1gW5b20FWxLSCCUnQA7sOPppeh1+EZIs89P71zFp67u85XyYTQ99ydY+RzP1iLM4pFdjquTwI0dwxTH9uG7uJoyLricT2DksjSnnXct3T/+N9x41ACtbxARWvTQQTQVNlahI6A+9/8S4vpa7t8fgXiR06UPprvUN2z/n/c/Y+OpLzPrxm2g/7qDm/rs48dmX2zzOmO6DcaZ0Zcsx46m/TXf/9zN77R/DlFHV9gP2spMSWT3NcoW3NXZuGAYCCeVXyKoX6stQzESO7nIx35c8xdb4FdyxZ0XD8ge1uzhh9Cls/te3eIMxOKMB1s6E1oHUoaIqAFyLTXYunIWOjoGOIRmYioGpCFAEqNZTtawq1ESF38ZOPozUnh0PqcjhMGaLdFotZAWP79otKFi8GdXtZNxRKaTqe1i20W2VNWhBYPV2yt/ZieTwguxvf4fR+B+pk/1qYjQ8rLS7jqkDOgMOPwZvXNRIMUCS2w40dXvdjD6xdbxMS7555V2KtjUea4nhZrfoQnW4mnhXPAWBCiR0zu5/LjcNaV6kUak2+CjvQbxq+3EkYyZNZPakiQy/+WNcHRyiojiIyd3E1a/cS7qvbUOguNjPp3ctAeCPz09ClmVyZ61kxudVZD75JOmj+iAiYUQ4ZE2RMHMeswyXoObh9ZsWtN6vcygaO7j77rsB6OVWyPU1uqeurqrBOPkdlF6T2m+8zX7HNlL2E009KfVE0HFL1o9ekiScbg9JLjfLX/4E19aFXF7uhDT40RjBLu9gLr/52VaCTiHTS1Z/GP6Hs/bahndvf4OwV8MVERw2cTmKx3raNo0IZqQaM1zF9iWXU6u0Lg4WiVg3CpfLzSMzN/P8T5aOwKCMOAZ08RHWDcK6yba64xkcP4OwehRdlI/Rgis71T91WhAftXgJ8ERaGu9sycHndHDhkD6csacOp2ndaLpl9G24IQw+5maWJmQxZukDDDS2cHWwjsr5X3Nt6UP8cew0dMPkrE9uYfuh2/AfOoW/phxKxoCRTOs9HICLDp0A7y4GYNegJJyTJ1gXvFAYIhGctWEIgrSmGo6PNlQIK8AxiizLDLnqj3QZdziFy5cya+aXpPRuLfjVlK03nYlSZmCkKgz8cSmKq+1g4XpMU8c05b1WwO0osE83LKNP3Yd01aYYciWPHX8Fj3EFmqER1kIEw0GO+WIqt+28B3NuCYeGh1qjCkf7iOuTRnx2a29S0PBTHMzFTISY5ESEJqzQC11B0WRUQ0aOSBAGTJAEmLIObghpdXttpyRMzBYZRZ54N5IwqAk5+eLNgiZLLH+halgGuFZaS/Hjqxu35fAgqXtIOL394TEj6kEpWBTk4ZXvNZMdanqeiGgphvHBs8j3FvL8o2+iKCqqojJo8AAOO3p4w7prZlmZMcG62sb9GBKSsu86KRWlOSAMXJ44tLCJ4miMa3m4Twrnb4NTFs/jp4knUxay9pfojGu1HS36+3N2QtdFlxSccvuepYhuIACvs/3zed7CPQ3vG+LYtOg57HYge2PA22gEC11n0vxzqInpju/Bp/Gk+KySAxLs3lRB7toyAmo2mlYCQJEZxy6/yvXBqZx11wMgW22xQ2MPPmwjZT/RUhMFIIKGT2qeKvn0ZU+hB34kNfswRo/wweYfmHLHTGRn65RgIxJGFx5cns4pkeoiFp8vlSOnbrOK40SRFSeyJxU8qYCEIlr/VMtrrfTCE9/aQci0LhoT+qbw8JmHkBHf9GloNPAIAG/MKMRJ+5LaTZmeu4AS0jkvdgfHDZ3CcUOHsGTHTq7YkAtxSThN6xhVT+MFNDU1i9QTboYTbgZDR33b8qC8uO5pdlWX8vOeH6lVrLpDz5/9MIme2OY7jVgX7Liv3+e4vq2FqrZ9Mp/tszWSs1rU5GhD3yHlkGH88swTuAQces317R6nP28j8mzrRjlo3tpOZdeYZghhKjjUzqea+v0V+P0FVmVYSaEuZF3g1xXXUVpdgiJbsQCqrOJ2ulE7EOBSRBxdHWMa/nYoDhyKg9ee/ydGsnUzWu3dzKBBQ+h92pENNWjaQpMjzCv6iMnHX83g4/YewwFQPmsmLARXfMdVnA1NQ5iilT6hKz6W828aRN2eUoxQmIg/QqguQt3CecirlvDWI33B7UbFgcOTiYpMppFAX7dCxq1nIXdkHEb1SzxaLKbaQgpfCCvYV5KQ65zE1qUQSwZlhps6sxpTGIREDcUL9lBVU0VqajK1lbUsnfshChInXNsoGCZMCbkdT0p7bF+3nO+fq2kyJ5O4Lo1G2pTMUVxc+i1vV/VgwryvSYkGBye4WvdzfcmE8e+dwPWH3MqVo9uPu9KRcSrtVwzWdB0TGXcHQ36nn9iHl77PB2ioHK9HjRSlRQ2w0JYt5JxyKjKQULOTflOyqQxXUh4sxxAGXcIaPVcahMtKiaxeSM2AvpwzwIrfOWLaSaiyfRs8mLG/nf1Egb+ANza8wZ9H/rlhXljScMvN3aeKUo4hJ3HRQ7ex5+2H0IUDtQ0DBUCrtdzQrjbk5ttCjyi4PI5mBkqrdUQYJXpaVJbks+Hdm1D1IKmRav6o9GVuwhlosovBXeN5+rw2ZLKbYgYRnRzXLQhbRsi5WY1qou9s2kZpnBX82X+XpZnQMuK/AUVFFlbpMNNRwIzCx0EBYTq5cegjrQ0UwNCsJ2iHq+1tqlhPj474RkOyLWOzntKqcmJllUhVJQ5P29t0JlhDR9Kx/Tqd/muaYUxTQe2kkRIIVDB/wXgcjsZ05YDmAR7mp106o/+1rNn6Se4alt95TrvtUc1kRIuSB0IIfoxY7vg+WhdGHn4YfQ/vOPMCQAtZbXI4O1/rJBLN7HJ52zdSagsKyJl6LPG6TvXw4a2WJ/TtRkLf5lLm6wNbUH7OIyVhNJqqohsmuuRnpxRkh1LMPBN8D+Yyru9IUntmoHoduN0eEtIScfm8lGzcza5tuZQru+k/4BBOv6h1dkw9j9z4BpCCKUU4+aRjGHGklfX05nMfk1u6kSVrf2pcuUc/ensScDUpIGoaEg7Hvhkpm1Z8DhxNr3HFpHVPJRIO0bVP84DnOwYdiWPDD2wKKqwu2UwMkORs3c8XDjuGHVU5rK37kjXFm4COjZSOQo4imoGQJBwdGLNzF1kGitQrtiFY2ohKGSgtNp5zyqkN78++VYV3hlufRXCo18CsS+Na4zZyIhsoHpLNEVtzuP74IPkJfTh3TI/2G2pzUGAbKfuRQ1KaF+eLSDoeubkB0rVvd3JWbbDG0UO1aHjb/ZI0v2WkqJ7O3bz0iIrT23EFUQMNRagEAnVsefVyjowsYot7GLqqMiW1hpuuPwpF7expE0KSW7uO22KDP0I3qZixaY0Xv6PSU/g06uHPz+wJlSB1MCYekAQ3ph7BBcc+i27qBDWDBLe33ZuvHr35Od1tD7d0P+kImP0LldvyASsIUAjRbg2VjPSubC4t4OU/X0nv5HQOPf9iuk2Y1Gwdf/5mhCQwtuRi6hpyB3Em9RhGCNNUcXSikjHAosWjcTjA47mZ2JgEwNLsePy4GgxHAkLCqmpsmny0opJ1xUkdqn/KKJg0l+SXJIn7Tv83d829nV1qCf/c8iA+l4+Jh3asuKpF+9zh3nsl23oiUcPG1UYhunr8RUU4dJ3aY6fS++r2slyaU1hSSiYw7YYbiGki0R6orGP7ys34q2vZvGMrs7YugMaST0hCwolKWIoW/XOApKUwmLaLbwJkZWdRuiXCn55trkJ88R/PRAvr5O8qQZKgKHcL3y+aT25tCe+9+BznXfVHZFlGmDLKPg73hMKWFzMtK4lDJx/d5jpxLh8PjDyDyR+NJyZUBcCYlNbBsf1Tu/HeGXcz9K0vcXVgLAsh0FFwduRN03VEi7plwbDOO2+sw18YwFncmG139JmNbTGihUodTTLD9LLGoPPeP/5Ar+XXETEiRMwIoxx7mOrTqdgzjk1+B+VOA4SC/MTD3DjluE7HEdkcWGwjZT+R5k0jqAf5Jf8XZElGlmSCio4cMshduyqagqeQ3C2BnJUR5r0/l6EV6/BI1ZD7S5vbrN1aBsSxfZsDupVbNxrZUreUZECSLMlsCUwzgKG5CQR/oqZ2SJPx8qY3J4lIpIy4Oh3vI90YIyTe7nE/o46/BAnwIthc0jqIsD3ngiSCJKoFlJf/DJIc3We9cJXcOE+S2BBJokAksqK6cfuF7lios0TcMsNWnIyxZzlKbVETQ8F61fQQtbLMwlAhh1ZstrQ4kCgMWrEBlhplVKMyuqxud46lmr2niHBdVGVPalR7rT+synKdmjKrHaZuIowIuWtWIkVTJmVZQZJlDv3r3+mTs5N1n33ItooStj33GBcEQyT27Wcdsyzjjc3Gdc54Ih8uoGzN1/gGtwxRbk1dXZ71fddqVEUr87X06DT926GehKZ/TVysRnbPxifn3m1s+/t13wNJrNpdZSnXKtYTrkORUBUZh2ylr4bNaipCFVFhLEs0q0u3nrx4wbsEIn5O/fo0pi94lyw1A0mWkSQrpRTZqlNTPy9/s+URK8/fTUnuzmZtaTCUmhhMElBeasURFBeXEIiK+jU9ZiEEVZs3IwGJLhexhkFw7VrrxGyyTlSEpP7DeCus4Zm83btxVFdH5eatydc7hXgplW6jelNXUUNVUQWyJGMYBls3b8GhOsju05OeQ/vyymuvUB0W+KvDzY+j6aGYIEzw+9v6/QjSMhMASO02lpLyQlZt3c62olJW/TSb3oOHouuJ1FZCVUmAVtHqtHU+wJQTn+TDVUtZ/UM+3QZ+hSSp1iSryPXvo38f16U/7+YuIcnhoKAuz/rFNBWYQ0I3BJJkUhuEHaV10a4VDfsTgGaY1kGHg5Ttbp5NU98vqzfn0QuJ7SV1FOyqZt1rWxrWaWr+pE7oQnqcp+G3V7OrFPAR2boZR4wbJInQZks1N3bSJJyZmXyV+VXD58sLq1m+6mgOKz4RfJCf8Te6n9+frr07Vti1ObiQREf+6wNATU0N8fHxVFdX4/PtXdr6P4XzZ5zPurJ1reYfvjaJfntaextUz0RuyL6/w23mhEbxbdU/96kd3Q5/nrjMVR2uk7U7wGdbT+YncxibRFaH63bEX0a+wNCUTXtfEbhA+qzD5Q9te4JLC75qd3mtJHF4dvd9at8tnxgcur39019TPcw/8rFm8/TgIvTQok7vY/CeUrLKa9pcVvRwBLMTjqZI2MOSJWd2ep9gcvgRH6Ioe0+L/XDzafyQN7nDdTyZb6LGbd7rtk79OYOEun2XaN8boS5ZaIkdC2qllpQwZc7cfd62rih8cfppmP9PNVFf5UBc4Y7baMghKtKWdn6jwiR28yokBO7Ev/2/2jfg7Cv3us43VQ5+rN27Zy+450L02ra0pRs5sehbegZ3tbu8VonhzR4X8/eqfUvzVfQgExfc1Gp+xgP3k3CGlSa+qS5IV5eDd/9kldxIUyXG9/KR8ofBqImd9+DZ7Du/x/3bNlL2E2EjTFWoClOYGMKwZJkjYZKFL6owaUZLtpsYmoErNhkpUIk3tA01Jbv97epONKcl82zVFGl8ahRCYJqN7yNaGTFJdS1GK5p//cU1AWr1rFYaAW2NBki0ntl0PSE0eiZFohklIhrX0Pja9H1JxCDfTCG5rUwXIQiG/GRVbSQ5LrlxJw2nrnXsWwKFhOPScaseRHRe/T/rf/N5oqYO3/KtdOk7rPGpO9qPVvMEVUEHIiO74SFcCIHTHcbrU6P1WwxMw0SYlqKuaRgN87XyMpKdHivt1zStoE5hIgydgFKA0rfjas/1GIbJ2rVFpKT0JzY2lrYEzVr+bRglJCc7Wql6tvzOTGFSVJeEpCSiGSa6KSwlW8NENwS6aVLsL8IZU0Ci14Uc9X7JkmwFhGK9eoWLXp4eUXVb63wWovGcbqh3o+v4qyrxJiS0zn5p/CP69VpvdF2nMhgiKS29wdPR1rErpWXEOx2N54dU77lr4aWpn4Cw0wk+X8Pvx6xva5PJrP9ttvO+dFctSb4uuNyO6HlWfxyi4RTVNI2aUDmxKY3O6/aG2CRJsq4D/loSYmMxdB1/VRh3XDecbk/9F9l4iE2+3TZecMcp+FIcCKFFayMZCNN6L4SOaWoIYRDWA+wOa8iKp9FL1fQ3IwRF1RrJam+UaLCpFFXvbWyHhO6voZsSiNbHafxe6/vCME32RJyovmQCZUHkwhC9+yc1/eobPWANf4MQJmphDj5XpEGhV3a7kRJjUbMyqK3dxAMrnufDyPn0TMzkkUIP+VsryRyYxGGntuVHtPmtsY0UGxsbGxubJixffibS02vxLlU4/lTL8ykBOQ/tvSK1zW/L73H/tiOHbGxsbGz+YxkoT8a71PIYDnblMSg9yFPn9NvLp2z+U7ADZ21sbGxs/mOJ6XMaA2/8HM54lRlJPQ90c2x+Y2wjxcbGxsYGgLWzZ/LDy88e6GZ0SIErg697nEpYVRrSimIif6HHXV9zbPls6qNxGhMAo3+3DORpeKnP5hPo4TDn3PMwmQNaFG21OWDYwz02NjY2NgDsWLEP2UcHiN2ebkR0kEIGUthEipgEkNkc0zsayK5jRosrGrqOoWkYmoauRawpEramsDVp4RBaOIQerU+2dVHruj82Bw47cNbGxsbG5j8C0zCprQijOmQ0XSMS1giHwsjCCaZE174JHQoT2vy+/B73b3u4x8bGxsbmPwJZkYlPrZdH6HxpBZv/XOzhHhsbGxsbG5uDEttIsbGxsbGxsTkosY0UGxsbGxsbm4MS20ixsbGxsbGxOSixjRQbGxsbGxubgxLbSLGxsbGxsbE5KLGNFBsbGxsbG5uDEttIsbGxsbGxsTkosY0UGxsbGxsbm4MS20ixsbGxsbGxOSixjRQbGxsbGxubgxLbSLGxsbGxsbE5KLGNFBsbGxsbG5uDkoOuCrIQArBKPtvY2NjY2Nj8Z1B/366/j/8WHHRGSm1tLQDdu3c/wC2xsbGxsbGx2Vdqa2uJj4//TbYlid/S5PkNME2TgoIC4uLikCTpN99+TU0N3bt3Z/fu3fh8vt98+/+t2P3267D77ddh99uvw+63X4fdb7+Olv0mhKC2tpauXbsiy79NNMlB50mRZZnMzMzffT8+n88+GX8Fdr/9Oux++3XY/fbrsPvt12H326+jab/9Vh6UeuzAWRsbGxsbG5uDEttIsbGxsbGxsTko+Z8zUlwuF3fddRcul+tAN+U/Crvffh12v/067H77ddj99uuw++3XsT/67aALnLWxsbGxsbGxgf9BT4qNjY2NjY3Nfwa2kWJjY2NjY2NzUGIbKTY2NjY2NjYHJbaRYmNjY2NjY3NQ8j9lpKxcuZJjjjmGhIQEkpOTueqqq6irq2u2jiRJraYPP/zwALX44KAz/VZPeXk5mZmZSJJEVVXV/m3oQcbe+q28vJzjjjuOrl274nK56N69O9dff/3/fN2qvfXbmjVrOO+88+jevTsej4eBAwfy1FNPHcAWHxx05nf65z//mUMPPRSXy8Xw4cMPTEMPMjrTb3l5eUybNg2v10taWhp///vf0XX9ALX4wLN161ZOOeUUUlJS8Pl8HHnkkcydO7fZOrNnz+bwww8nLi6OLl26cMstt/yqPvufMVIKCgo4+uij6dOnD0uWLGHmzJls2LCBSy+9tNW6b7zxBoWFhQ3Tqaeeut/be7CwL/0GcPnll3PIIYfs30YehHSm32RZ5pRTTmH69Ols3bqVN998kx9//JFrrrnmwDX8ANOZfluxYgVpaWm8++67bNiwgX/+85/ceuutPPvssweu4QeYffmdXnbZZZxzzjn7v5EHIZ3pN8MwmDZtGpFIhIULF/LWW2/x5ptvcueddx64hh9gTjzxRHRdZ86cOaxYsYJhw4Zx4oknUlRUBFgPEieccALHHXccq1at4qOPPmL69On84x//2Pedif8RXnrpJZGWliYMw2iYt3btWgGIbdu2NcwDxBdffHEAWnhw0tl+E0KI559/XkycOFHMnj1bAKKysnI/t/bgYV/6rSlPPfWUyMzM3B9NPCj5tf127bXXismTJ++PJh6U7Gu/3XXXXWLYsGH7sYUHJ53pt2+//VbIsiyKiooa1nnhhReEz+cT4XB4v7f5QFNaWioA8fPPPzfMq6mpEYD44YcfhBBC3HrrrWLUqFHNPjd9+nThdrtFTU3NPu3vf8aTEg6HcTqdzYoeeTweABYsWNBs3euuu46UlBTGjBnD66+//puWnf5Po7P9tnHjRu69917efvvt36yw1H8y+3K+1VNQUMDnn3/OxIkT90sbD0Z+Tb8BVFdXk5SU9Lu372Dl1/bb/zqd6bdFixYxdOhQ0tPTG9Y59thjqampYcOGDfu3wQcBycnJ9O/fn7fffhu/34+u67z00kukpaVx6KGHAla/ut3uZp/zeDyEQiFWrFixT/v7n7mbTJkyhaKiIh599FEikQiVlZUNrqfCwsKG9e69914+/vhjfvjhB8444wyuvfZannnmmQPV7ANOZ/otHA5z3nnn8eijj9KjR48D2dyDhs6ebwDnnXceXq+Xbt264fP5ePXVVw9Ekw8K9qXf6lm4cCEfffQRV1111f5s6kHFr+k3m871W1FRUTMDBWj4u354438JSZL48ccfWbVqFXFxcbjdbp544glmzpxJYmIiYBlxCxcu5IMPPsAwDPLz87n33nuBfT8f/+ONlH/84x9tBrs2nTZv3szgwYN56623ePzxx/F6vXTp0oWePXuSnp7ezIq+4447OOKIIxgxYgS33HILN998M48++ugBPMLfh9+y32699VYGDhzIhRdeeICP6vfntz7fAJ588klWrlzJV199xY4dO/jb3/52gI7u9+P36DeA9evXc8opp3DXXXcxderUA3Bkvy+/V7/9t2P3277T2T4TQnDdddeRlpbG/PnzWbp0KaeeeionnXRSgwEydepUHn30Ua655hpcLhf9+vXjhBNOANjnfv2Pl8UvLS2lvLy8w3V69eqF0+ls+Lu4uJiYmBgkScLn8/Hhhx9y1llntfnZGTNmcOKJJxIKhf6r6jr8lv02fPhw1q1bhyRJAAghME0TRVH45z//yT333PO7Hsv+5Pc+3xYsWMD48eMpKCggIyPjN237geT36LeNGzcyefJkrrjiCh544IHfre0Hkt/rfLv77rv58ssvWb169e/R7APOb9lvd955J9OnT2/WVzk5OfTq1YuVK1cyYsSI3+sw9iud7bP58+czdepUKisr8fl8Dcv69u3L5Zdf3iw4VghBYWEhiYmJ5ObmMmjQIJYuXcro0aM73S513w/l4CI1NZXU1NR9+ky9q+7111/H7XZzzDHHtLvu6tWrSUxM/K8yUOC37bfPPvuMYDDYsN6yZcu47LLLmD9/Pr179/7tGn0Q8Hufb6ZpAtYQ2n8Tv3W/bdiwgSlTpnDJJZf81xoo8Pufb/+t/Jb9dthhh/HAAw9QUlJCWloaAD/88AM+n49Bgwb9tg0/gHS2zwKBANDaIyLLcsP1qx5JkujatSsAH3zwAd27d2fkyJH71rD/T5TvfxrPPPOMWLFihdiyZYt49tlnhcfjEU899VTD8unTp4tXXnlFrFu3Tmzbtk08//zzwuv1ijvvvPMAtvrAs7d+a8ncuXP/57N7hNh7v82YMUO8/vrrYt26dSInJ0d88803YuDAgeKII444gK0+8Oyt39atWydSU1PFhRdeKAoLCxumkpKSA9jqA09nfqfbtm0Tq1atEldffbXo16+fWLVqlVi1atX/ZJZKPXvrN13XxZAhQ8TUqVPF6tWrxcyZM0Vqaqq49dZbD2CrDxylpaUiOTlZnH766WL16tViy5Yt4qabbhIOh0OsXr26Yb1HHnlErF27Vqxfv17ce++9wuFw/KrM2f8pI+Wiiy4SSUlJwul0ikMOOUS8/fbbzZZ/9913Yvjw4SI2NlbExMSIYcOGiRdffLFZetr/Invrt5bYRorF3vptzpw54rDDDhPx8fHC7XaLvn37iltuucXut73021133SWAVlNWVtaBafBBQmd+pxMnTmyz73JycvZ/gw8SOtNvubm54vjjjxcej0ekpKSIG2+8UWiadgBae3CwbNkyMXXqVJGUlCTi4uLEuHHjxLfffttsncmTJzdc28aOHdtqeWf5j49JsbGxsbGxsfnv5H8rfNnGxsbGxsbmPwbbSLGxsbGxsbE5KLGNFBsbGxsbG5uDEttIsbGxsbGxsTkosY0UGxsbGxsbm4MS20ixsbGxsbGxOSixjRQbGxsbGxubgxLbSLGxsbGxsbE5KLGNFBsbGxsbG5uDEttIsbGxsbGxsTkosY0UGxsbGxsbm4MS20ixsbGxsbGxOSj5PyL05MbrU6GXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in range(nTracts):  #optional full state visualization after squish\n",
    "    if t not in allCutVTDs and t not in skipList:\n",
    "        plotPoly(vtdGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9715564f-3b60-43e5-9f1f-02f3dcee3933",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = 22  #pick a county for visualization if desired\n",
    "print(\"here are the faint(original) and squished shapes for county\",c)\n",
    "for t in countyTractList[c]:\n",
    "    plotPoly(vtdGeom[t])\n",
    "    plotPoly(tractGeom[t],0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "760bc48e-0236-4ad7-b3b8-d74795009f53",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"alternate simple approach for Florida Keys - translate all toward county CP\")\n",
    "c = 43\n",
    "vtdGeom = tractGeom.copy()  #a reset\n",
    "nTranslated = 0\n",
    "newCP = Point(-81, 25.1)\n",
    "for v in countyTractList[c]:\n",
    "    if clipPoly.contains(tractCP[v]):\n",
    "        xDelta, yDelta = newCP.x - tractCP[v].x, newCP.y - tractCP[v].y\n",
    "        xOFF, yOFF = 0.95 * xDelta, 0.95 * yDelta\n",
    "        vtdGeom[v] = translate(vtdGeom[v],xoff= xOFF, yoff=yOFF)\n",
    "        nTranslated +=1\n",
    "print(\"I translated\",nTranslated,\"vtds toward the center of county\",c)\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]\n",
    "countyGeom[c] = vtdGeom[countyTractList[c][0]]\n",
    "for v in countyTractList[c]:\n",
    "    countyGeom[c] = countyGeom[c].union(vtdGeom[v])\n",
    "    plotPoly(hdCP[v].buffer(0.03))\n",
    "plotPoly(countyGeom[c])\n",
    "unsquishedMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "#plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4ef91f8f-dabb-4c58-a8a4-bba8e725e586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to use squished shapes (e.g. for MD, MN), enter 0 for unclipped states 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on confirming HD center 0 is inside the map's convex hull\n",
      "working on confirming HD center 500 is inside the map's convex hull\n",
      "working on confirming HD center 1000 is inside the map's convex hull\n",
      "working on confirming HD center 1500 is inside the map's convex hull\n",
      "working on confirming HD center 2000 is inside the map's convex hull\n",
      "working on confirming HD center 2500 is inside the map's convex hull\n",
      "working on confirming HD center 3000 is inside the map's convex hull\n",
      "working on confirming HD center 3500 is inside the map's convex hull\n",
      "working on confirming HD center 4000 is inside the map's convex hull\n",
      "working on confirming HD center 4500 is inside the map's convex hull\n",
      "Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note the MAP convex hull.  Overwrite with your own polygon if desired.\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 - draw polygonal HDs.  #REQUIRES CONSISTENT TREATMENT OF CCB'S \n",
    "#For GA, MA, MD, MN, NC we draw HDs for ALL vtds, even if they are in a CCB.  We use the clipPoly's to squish in the corners for these HD shapes\n",
    "#Not sure if below would work if we have cut districts AND squished-in corners\n",
    "#We normally draw for each (squished or orig) vtd.  Or, read in tract or blockgroup geometries, use those pops and unit centerpoints\n",
    "\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]  #remember, this is after any squish operation\n",
    "print(\"Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\")\n",
    "useSquish = int(input(\"enter 1 to use squished shapes (e.g. for MD, MN), enter 0 for unclipped states\"))\n",
    "if useSquish == 1:\n",
    "    convexMAP = hdCP[countyTractList[uncutCountyList[0]][0]].buffer(0.1) \n",
    "    for v in range(nTracts):\n",
    "        if v not in skipList and v not in allCutVTDs:\n",
    "            convexMAP = convexMAP.union(hdCP[v].buffer(0.1))\n",
    "else:  #build the map via simple union of counties not wholly in fully cut-out districts\n",
    "    convexMAP = countyGeom[uncutCountyList[0]]\n",
    "    for c in uncutCountyList:\n",
    "        convexMAP = convexMAP.union(countyGeom[c])\n",
    "    #convexMAP = MAP.centroid\n",
    "    #for c in range(nCounties):\n",
    "    #    if c not in allFusedCounties:\n",
    "    #        convexMAP = convexMAP.union(countyGeom[c])\n",
    "MAPhull = convexMAP.convex_hull.buffer(0.01*convexMAP.area**0.5)\n",
    "plotPoly(MAPhull)\n",
    "popHDlist = list()\n",
    "for t in range(nTracts):\n",
    "    if t not in allCutVTDs and t not in skipList:  #keep low-pop tracts as VEST may have assigned voters to them\n",
    "        popHDlist.append(t)\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on confirming HD center\",t,\"is inside the map's convex hull\")\n",
    "    if hdCP[t].disjoint(convexMAP.convex_hull):\n",
    "        plotPoly(hdCP[t].buffer(0.03))\n",
    "        hdCP[t] = nearest_points(convexMAP.convex_hull,hdCP[t])[0]\n",
    "        plotCenter(\"m\",hdCP[t])\n",
    "    else:\n",
    "        plotPoly(hdCP[t].buffer(0.01))\n",
    "plotPoly(convexMAP)\n",
    "plotPoly(convexMAP.convex_hull)\n",
    "plotPoly(MAPhull)\n",
    "for c in allFusedCounties:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "print(\"Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Note the MAP convex hull.  Overwrite with your own polygon if desired.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8393e96a-75c6-48ec-94e8-1f6fe58d31df",
   "metadata": {},
   "outputs": [],
   "source": [
    "nHDs = nTracts  #need to run this if we start option 2 from a vest list, not a previously run HD poly list\n",
    "HDcountyNo = countyNo.copy()\n",
    "populatedTractList = popHDlist.copy()  #nomenclature equivalency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bcaf6ea2-1c3b-48c1-9ad6-937d7b7e4385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let us establish the (post-squish) point-point distances for all HD centers, about 8 min for 4000-unit state\n",
      "working on tract-tract distances for unit 0 out of 4604 . Time = 0\n",
      "working on tract-tract distances for unit 400 out of 4604 . Time = 113\n",
      "working on tract-tract distances for unit 800 out of 4604 . Time = 218\n",
      "working on tract-tract distances for unit 1200 out of 4604 . Time = 312\n",
      "working on tract-tract distances for unit 1600 out of 4604 . Time = 397\n",
      "working on tract-tract distances for unit 2000 out of 4604 . Time = 471\n",
      "working on tract-tract distances for unit 2400 out of 4604 . Time = 534\n",
      "working on tract-tract distances for unit 2800 out of 4604 . Time = 586\n",
      "working on tract-tract distances for unit 3200 out of 4604 . Time = 627\n",
      "working on tract-tract distances for unit 3600 out of 4604 . Time = 658\n",
      "working on tract-tract distances for unit 4000 out of 4604 . Time = 678\n",
      "working on tract-tract distances for unit 4400 out of 4604 . Time = 688\n",
      "All done; total elapsed  690 seconds for MO with 8 districts to map\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "our maxD was 6.1418198690657055\n"
     ]
    }
   ],
   "source": [
    "#continuing option 2 ....... ##########  THIS IS FOR TRACT - TRACT, despite the \"unit\" nomenclature\n",
    "print(\"Let us establish the (post-squish) point-point distances for all HD centers, about 8 min for 4000-unit state\")\n",
    "#NOTE: FOR LARGE STATES, RESTRICT THE SEARCH TO COUNTIES WITHIN A 3/SQRT(nDistrict) DIAMETER OF THE HOME UNIT\n",
    "uuDist = [[int(maxD+1) for t in range(nHDs)] for t in range(nHDs)] #default: too big to capture\n",
    "closeCountyList = [list() for c in range(nCounties)]\n",
    "closeCountyDist = maxD   #min(maxD,3./float(nDistricts)**0.5 * maxD )  #use above maxD for these counties as well\n",
    "xScaleSquared = xScale*xScale\n",
    "if nDistricts < 10: #closeCountyDist >= maxD:  #small state\n",
    "    closeCountyList = [[i for i in range(nCounties)] for c in range(nCounties)]  #all counties are close enough\n",
    "else:\n",
    "    for c in uncutCountyList:\n",
    "        closeCountyList[c].append(c)\n",
    "        for cc in range(c+1,nCounties):\n",
    "            if cc in uncutCountyList:\n",
    "                if cc in neighborCountyList[c]:\n",
    "                    closeCountyList[c].append(cc)\n",
    "                    closeCountyList[cc].append(c)\n",
    "                else:    \n",
    "                    dist = getLongDist(countyCP[c], countyCP[cc],xScale)\n",
    "                    if dist < closeCountyDist:\n",
    "                        closeCountyList[c].append(cc)\n",
    "                        closeCountyList[cc].append(c)\n",
    "startTime = time.time()\n",
    "\n",
    "for i,t in enumerate(populatedTractList):\n",
    "    if i %400 == 0:\n",
    "        print(\"working on tract-tract distances for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime))\n",
    "    uuDist[t][t] == 0.\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "                dist = getLongDist(hdCP[t], hdCP[tt],xScale)\n",
    "                uuDist[t][tt], uuDist[tt][t] = dist, dist\n",
    "print(\"All done; total elapsed \",int(time.time()-startTime),\"seconds for\",STATE,\"with\",nDistricts,\"districts to map\" )\n",
    "plt.hist(uuDist)\n",
    "plt.show()\n",
    "print(\"our maxD was\",maxD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2f392acb-ba52-4343-80ae-a58ba0b57366",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similar to above, but for angles\n",
      "working on unit-unit angles for unit 0 out of 4604 . Time = 0\n",
      "working on unit-unit angles for unit 400 out of 4604 . Time = 61\n",
      "working on unit-unit angles for unit 800 out of 4604 . Time = 116\n",
      "working on unit-unit angles for unit 1200 out of 4604 . Time = 166\n",
      "working on unit-unit angles for unit 1600 out of 4604 . Time = 210\n",
      "working on unit-unit angles for unit 2000 out of 4604 . Time = 247\n",
      "working on unit-unit angles for unit 2400 out of 4604 . Time = 279\n",
      "working on unit-unit angles for unit 2800 out of 4604 . Time = 305\n",
      "working on unit-unit angles for unit 3200 out of 4604 . Time = 327\n",
      "working on unit-unit angles for unit 3600 out of 4604 . Time = 343\n",
      "working on unit-unit angles for unit 4000 out of 4604 . Time = 353\n",
      "working on unit-unit angles for unit 4400 out of 4604 . Time = 359\n",
      "All done with angles; total elapsed seconds = 360\n"
     ]
    }
   ],
   "source": [
    "#continuing option 2 #NOTE FOR MO - DOING THIS WHILE OTHER COMPUTATIONS ARE OCCURRING, SO TIME WILL BE SLOW\n",
    "print(\"similar to above, but for angles\")  #convention is angle[a][b] is from a to b\n",
    "uuAngle = [[0. for t in range(nHDs)] for t in range(nHDs)]\n",
    "\n",
    "pi = 3.141592653\n",
    "twoPi = pi*2.\n",
    "startTime = time.time()\n",
    "for i, t in enumerate(populatedTractList):\n",
    "    if i %400 == 0:\n",
    "        print(\"working on unit-unit angles for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime)  )\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "            angLE = math.atan2(hdCP[tt].y - hdCP[t].y, xScale * (hdCP[tt].x - hdCP[t].x) )\n",
    "            uuAngle[t][tt], uuAngle[tt][t] = angLE % twoPi, (angLE + pi) % twoPi\n",
    "print(\"All done with angles; total elapsed seconds =\",int(time.time()-startTime) )\n",
    "#plt.hist(uuAngle)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4cc54eba-ca37-4680-9699-37332133afb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "769364.125 8 6154913.0 8.0 6154913.0\n",
      "6154913.0 6154913 1.0\n"
     ]
    }
   ],
   "source": [
    "print(r3(aDP), nDistricts, statePop, statePop/aDP, trueStatePop)\n",
    "HDweight = [0 for t in range(nHDs)]\n",
    "for t in populatedTractList:\n",
    "    HDweight[t] = tractPop[t] / trueStatePop #skipping the cutList tracts\n",
    "print(statePop,np.sum([tractPop[t] for t in populatedTractList]), r5(np.sum(HDweight))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "dac2348a-060a-40a9-beeb-02ff70ec1aa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUN FOR ALL STATES.for extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\n"
     ]
    }
   ],
   "source": [
    "print(\"RUN FOR ALL STATES.for extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\")\n",
    "opt2nbrCtyList = [neighborCountyList[c].copy() for c in range(nCounties)]\n",
    "for i in range(nClips):\n",
    "    cList = toSquishCountiesList[i]\n",
    "    for c in cList:\n",
    "        for cc in cList:\n",
    "            for ccc in neighborCountyList[cc]:\n",
    "                if ccc not in opt2nbrCtyList[c] and ccc != c:\n",
    "                    opt2nbrCtyList[c].append(ccc)\n",
    "#for c in range(nCounties):\n",
    "#    for cc in opt2nbrCtyList[c]:\n",
    "#        plotPoly(countyGeom[cc])\n",
    "#    plotCenter(c,countyGeom[c])\n",
    "#    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3873aaa2-0c39-4ac0-99c3-92dd7c18c555",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\n",
      "For each Home District, we will consider a wedge as one-from-full when it is within 1069.49 of targetWedgePop\n",
      "I am working on HD number 0 of 4604 HDs.  Elapsed sec = 0\n",
      "I am working on HD number 400 of 4604 HDs.  Elapsed sec = 118\n",
      "I am working on HD number 800 of 4604 HDs.  Elapsed sec = 249\n",
      "I am working on HD number 1200 of 4604 HDs.  Elapsed sec = 367\n",
      "I am working on HD number 1600 of 4604 HDs.  Elapsed sec = 487\n",
      "I am working on HD number 2000 of 4604 HDs.  Elapsed sec = 585\n",
      "I am working on HD number 2400 of 4604 HDs.  Elapsed sec = 708\n",
      "I am working on HD number 2800 of 4604 HDs.  Elapsed sec = 846\n",
      "I am working on HD number 3200 of 4604 HDs.  Elapsed sec = 979\n",
      "I am working on HD number 3600 of 4604 HDs.  Elapsed sec = 1131\n",
      "I am working on HD number 4000 of 4604 HDs.  Elapsed sec = 1282\n",
      "I am working on HD number 4400 of 4604 HDs.  Elapsed sec = 1430\n",
      "1486.589 seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of vtd usage.\n",
      "vtd avg and sd usage are 1.00052 0.13939\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 .......\n",
    "# BELOW modified Dec23 to use inter-unit distances and angles, (counties still wedgeIntersxn) otherwise similar to HD2\n",
    "# --THIS ESSENTIALLY TAKES THE ORIGINAL TRACT-BASED HD centerpoints, and redraws HD2 districts after convexifying corners and convex_hull\n",
    "print(\"Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\") \n",
    "#this is a hybrid of HD2 and the organic code, in that all wedge tracts must be in a chain of neighboring counties\n",
    "#General sequence: draw 4 equi-angle wedges with random starting orientation.  If not all can fill a quarter aDP ...\n",
    "# ... find the closest map boundary point to the tract center point in the shortest wedge.  Orient the 0th wedge to face this point\n",
    "# ... determine the \"maxWedgePop\" for this short wedge and the other three wedges extended past the boundary\n",
    "# ...  if this results in only one short wedge, or opposing short wedges, adjust wedge angles\n",
    "# Regardless of whether we re-oriented or adjusted angles, compute each wedgePop for infinite radius\n",
    "#   ... then adjust other targetWedgePops if some found to be short.\n",
    "#   ... for non-shorted wedges, use bisection method to adjust radius to meet targetWedgePop\n",
    "#   ... once total HDpop within tolerance, add/subtract a tract fraction to fulfill HDpop\n",
    "#  create a list of fully used tracts per HD, save the tract# of the split tract and its fractional use\n",
    "#  Compute tractUse across all HDs at the end from the tractUsageLists.  Defer patching and partisan calcs to another code\n",
    "\n",
    "pi=3.1415926536\n",
    "#MAPhull = MAP.convex_hull #used for exitAngle calc.  Defined in an above block to allow user modification\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2.*pi / nWedges\n",
    "angle, angle2, wedgePoly = [0.]*nWedges, [0.]*nWedges, [dummyPoly]*nWedges  #start and stop angle of each wedge\n",
    "pt1, pt2, pt3 = [Point(0,0)]*nWedges, [Point(0,0)]*nWedges, [Point(0,0)]*nWedges #these four define the polygon wedge for a growing home district\n",
    "tractUse = [0.] * nHDs  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "HDtractList = [list()]*nHDs\n",
    "splitTractNo = [-999]*nHDs  #the tract that is only partially used to finish each HD\n",
    "splitTractUse = [0.]*nHDs  #and this is what fraction of the split tract is included\n",
    "HDvPop = [0.]*nHDs\n",
    "#HDweight = [tractPop[t] / (statePop - excludedPop) for t in range(nTracts)]  #relative weight of each drawn HD\n",
    "HDPoly1 = [dummyPoly]*nHDs  #final 4-wedge shape, unionized from blockgroups / tracts\n",
    "HDarea = [0.]*nHDs  #geographical area of each home district (after boundary trim)\n",
    "HDradius = [[0.]*nWedges for t in range(nHDs)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nHDs)] #final starting angle for each HD wedge\n",
    "angle0 = [-999.] * nHDs  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "avgTractPop = statePop/len(populatedTractList)\n",
    "tolerPops = [0.8 * avgTractPop, 0.5 * np.max(tractPop)]  #threshold gap before adding one more unit to a wedge \n",
    "tolerPop = tolerPops[0]\n",
    "print(\"For each Home District, we will consider a wedge as one-from-full when it is within\",r3(tolerPops[0]),\"of targetWedgePop\")\n",
    "tractPrintInterval = 400  #for tracking progress\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #HD2\n",
    "maxAngle = 1.8*pi/2. # limit to avoid wedges too close to half a pie\n",
    "minAdjRatio = 0.5  #min ratio of pop of adjacent wedge (to min wedge) to targetWedgePop to not consider this a corner HD\n",
    "maxUncap = 0.5 #the max ratio of uncaptured pop in a maxWedge vs. target wedge pop that we will not stretch to include (7/23)\n",
    "oppWt = 0.0    #the fraction of gap between normal targetWedgePop and the minWedgePop that we allow the opposite wedge to add to tgtPop = minWedgePop\n",
    "maxWedgePop = [0.] * nWedges  #wedge pop if we explode wedge to maximum radius\n",
    "printDebug = \"no\"\n",
    "codeStartTime = time.time()\n",
    "hdCPpop = [HDweight[t] * statePop for t in range(nHDs)]\n",
    "countyHDlist = [list() for c in range(nCounties)]\n",
    "for t in populatedTractList:\n",
    "    countyHDlist[HDcountyNo[t]].append(t)\n",
    "\n",
    "for kkkj, t in enumerate(populatedTractList):  #range(nTracts) : #loop on each tract. \n",
    "    hC = HDcountyNo[t]     #this tract's home county\n",
    "    HDtractList[t] = [t] #seed the list of tracts in this HD\n",
    "    wedgeList = [list() for w in range(nWedges)]   #list of each wedge's tracts, excluding home tract\n",
    "    barredList = list()  #list of wedge numbers for which we are barred from altering their targetWedgePops\n",
    "    if (kkkj % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on HD number {0} of {1} HDs.  Elapsed sec = {2}\".format(t,nHDs,int(time.time()-codeStartTime) ) )  \n",
    "    nActiveWedges = nWedges #active wedges have not run over boundary\n",
    "    wedgePop = [0.]*nWedges\n",
    "    targetWedgePop = [aDP / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    \n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to the midangle of our starting wedge\n",
    "    wedgeAngle = [avgWedgeAngle for w in range(nWedges) ] #reset to equi-angle when starting each tract\n",
    "    for nW in range(nWedges-1):\n",
    "        wedgeAngle[nW] += random.uniform(-0.0001,0.0001)  #this wiggle solves a later indexing ambiguity for corner tracts\n",
    "    wedgeAngle[nWedges-1] = 2.*pi - (np.sum(wedgeAngle) - wedgeAngle[nWedges-1] ) #squaring up to 2pi total\n",
    "    startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "    for nW in range(1,nWedges):\n",
    "        startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "    endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "    #  TRIAL LOOP TO SEE WHICH WEDGES CAN FILL TO TARGET POP w/equiangle wedges\n",
    "    maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]    \n",
    "    maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "    willFill = [0] * nWedges #would this wedge meet its target with infinite radius?\n",
    "    for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "        iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "        #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList) \n",
    "        nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "        maxWedgePop[nW] += (iCP + nonCP)\n",
    "        wedgeList[nW] = Li + Ln\n",
    "        if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "            willFill[nW] = 1\n",
    "     \n",
    "    if np.sum(willFill) < nWedges:  #at least one wedge couldn't reach its wedgePop target (went over boundary) ...\n",
    "        minW = maxWedgePop.index(np.min(maxWedgePop)) #.. so we will orient the 0th wedge to face the shortest wedgePop's closest boundary\n",
    "        exitAngle = getExitAngle(hdCP[t],maxWedgePoly[minW], MAPhull, startAngle[minW], endAngle[minW])\n",
    "        angle0[t] = exitAngle - 0.5*(2.*pi / nWedges)  #Now reset all angles based on new starting orientation.  All wedge angles still equal\n",
    "        startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "        for nW in range(1,nWedges):\n",
    "            startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "        endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "        maxWedgePoly = [buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]\n",
    "        \n",
    "        willFill = [0]*nWedges\n",
    "        #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation (wedge angles still constant)\n",
    "        maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop        \n",
    "        for nW in range(nWedges):   #... then add in-county and nonCounty wedge Pop from contiguous chain of counties\n",
    "            iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "            #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "            nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                    opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "            maxWedgePop[nW] += (iCP + nonCP)\n",
    "            wedgeList[nW] = Li + Ln\n",
    "            if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                willFill[nW] = 1       \n",
    "    \n",
    "    if np.sum(willFill) < nWedges:  #check if we need to modify wedge angles after possible re-orientation.  \n",
    "        # If so, re-draw maxWedgePoly's, re-calc maxWedgePops\n",
    "        nUnfilledWedges = nWedges - np.sum(willFill)\n",
    "        isChange, newInclA = getNewAngles(maxWedgePop, targetWedgePop, aDP, levelL, minAdjRatio, maxAngle, maxAngleRatio, nUnfilledWedges )\n",
    "        if isChange: #no longer equi-angle\n",
    "            newStartAngle = [0.5*(startAngle[nW]+endAngle[nW]) - 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            newEndAngle =   [0.5*(startAngle[nW]+endAngle[nW]) + 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            startAngle, endAngle, wedgeAngle = newStartAngle.copy(), newEndAngle.copy(), newInclA.copy()\n",
    "            maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], \n",
    "                                        maxD/math.cos(0.5*wedgeAngle[nW]), xScale ) for nW in range(nWedges) ]\n",
    "            willFill = [0]*nWedges\n",
    "            #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation and new wedge angles\n",
    "            maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "            for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "                iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])                \n",
    "                #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "                nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                        opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "                maxWedgePop[nW] += (iCP + nonCP)\n",
    "                wedgeList[nW] = Li + Ln\n",
    "            minW = wedgeAngle.index(np.max(wedgeAngle)) #should be 0th wedge, but playing it safe.  This is the 1st wide-angle wedge ..\n",
    "            oppW = int(int(minW+nWedges/2)%nWedges)   #and its opposite\n",
    "            if maxWedgePop[minW] > maxWedgePop[oppW] and maxWedgePop[oppW] < aDP / nWedges:  #minW and oppW are flipped after inclA adjmt\n",
    "                oppW = minW\n",
    "                minW = int(int(minW+nWedges/2)%nWedges)\n",
    "            if maxWedgePop[minW] < targetWedgePop[oppW]:  #we need to constrain oppW in this HD2 implementation; redistribute tgt to adjacents\n",
    "                maxNonOppW = np.sum(maxWedgePop) - maxWedgePop[oppW] #...but only if other wedges can pick up slack; need to check\n",
    "                minOppWedgePop = aDP - maxNonOppW  #cannot set oppW target below this or we won't reach aDP across all wedges\n",
    "                barredList = [oppW]\n",
    "                targetWedgePop[oppW] = max(minOppWedgePop, maxWedgePop[minW] + oppWt * (targetWedgePop[oppW] - maxWedgePop[minW]) )\n",
    "                tWPgap = aDP / float(nWedges) - targetWedgePop[oppW] \n",
    "                for nW in range(nWedges):\n",
    "                    if nW != minW and nW != oppW:\n",
    "                        targetWedgePop[nW] += tWPgap/float(nWedges - 2.)  #if oppW target is limited, allot to adjacent wedges\n",
    "            for nW in range(nWedges):\n",
    "                if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                    willFill[nW] = 1  \n",
    "    \n",
    "    if abs(np.sum(targetWedgePop) / aDP - 1.) > 0.001:\n",
    "        raise Exception(\"FAIL! Our target wedgepops\",targetWedgePop, np.sum(targetWedgePop),\"no longer add up to\",aDP)\n",
    "    \n",
    "    if np.sum(willFill) == nWedges:  #all wedges will fill.  Will any leave a small near-boundary remnant?  If so, pick up smallest remnant\n",
    "        remnantWedgePopFrac = [ ( maxWedgePop[w] - targetWedgePop[w] )/targetWedgePop[w] for w in range(nWedges) ]\n",
    "        if np.min(remnantWedgePopFrac) < maxUncap :  #lessen tWP's of *adjacent* wedges, then increase this wedge's target --> max\n",
    "            minW = remnantWedgePopFrac.index(np.min(remnantWedgePopFrac))\n",
    "            oppW = int((minW+nWedges/2)%nWedges)\n",
    "            #print(\"filling to boundary for tract,wedge, tWP, mWP =\",t,minW, r3(targetWedgePop[minW]), maxWedgePop[minW])\n",
    "            for nW in range(nWedges):\n",
    "                if nW != minW and nW != oppW:\n",
    "                    targetWedgePop[nW] -= (remnantWedgePopFrac[minW] * targetWedgePop[minW] ) / (nWedges - 2.)\n",
    "            targetWedgePop[minW] = maxWedgePop[minW]\n",
    "            #       OK, READY TO SOLVE FOR NON-FILLED WEDGE SIZES once we adjust targets for unfilled wedges\n",
    "    #print(t,\" prior to rebalanceTWP call, mWPs, tWPs are\",maxWedgePop, targetWedgePop)\n",
    "    targetWedgePop, willFill = rebalanceTWPs(maxWedgePop, targetWedgePop, barredList)\n",
    "    #if np.max(targetWedgePop) - np.min(targetWedgePop) > 0.02 * aDP: #debug\n",
    "    #    print(\"for tract\",t,\",  After rebalancing but before tweaks for blocky pop adds: willFill, targetWedgePops and maxWedgePops are ...\")\n",
    "    #    for w in range(nWedges):\n",
    "    #        print(w, willFill[w],r3(targetWedgePop[w]),int(maxWedgePop[w]) )\n",
    "    for w in range(nWedges):  #WHEW!  WE CAN FINALLY ASSIGN ALL WEDGES' POPS AND TRACTLISTS\n",
    "        loop = 0\n",
    "        if willFill[w] == 0:  #wedge is maxed out\n",
    "            wedgePop[w] = maxWedgePop[w]\n",
    "            #wedgeList[w] = ...  #we already captured this list = maxWedge list\n",
    "            HDradius[t][w] = maxD/math.cos(0.5*wedgeAngle[w])\n",
    "        else:  #need to solve for wedge radius to meet targetPop.  Use bisection as scipy minimize no faster\n",
    "            HDcpWP = hdCPpop[t] * wedgeAngle[w]/(2.*pi)\n",
    "            nonHDcpTWP = targetWedgePop[w] - HDcpWP\n",
    "            #wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeB(nontractTWP,t,hC,maxWedgePoly[w],tolerPop,tractCP, tractPop,\n",
    "            #                                                        countyNo,countyTractList,countyGeom, neighborCountyList,uuDist[t])\n",
    "            wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeC(nonHDcpTWP,t,hC, maxWedgePoly[w],startAngle[w], endAngle[w], tolerPop,\n",
    "                                                                    hdCP, hdCPpop,HDcountyNo, countyHDlist,countyGeom,\n",
    "                                                                    opt2nbrCtyList,uuDist[t],uuAngle[t])\n",
    "            popGap = nonHDcpTWP - wedgePop[w]  #gap between target and solved wedge pop; can be negative\n",
    "            notYetAdjusted, nextW = True, w+1  #looking to adjust a later wedge's target pop to minimize drift in total HD pop\n",
    "            while notYetAdjusted and nextW < nWedges:\n",
    "                if willFill[nextW] == 1 and maxWedgePop[nextW] > targetWedgePop[nextW] + popGap :  #can accommodate\n",
    "                    targetWedgePop[nextW] += popGap\n",
    "                    notYetAdjusted = False\n",
    "                nextW +=1          \n",
    "\n",
    "        HDangle[t][w] = startAngle[w]\n",
    "    #print(t,\"tract. After TWP adjustment, targetWedgePops are\",targetWedgePop)\n",
    "    HDtractList[t] = [t]\n",
    "    totPop = hdCPpop[t]\n",
    "    for nW in range(nWedges):\n",
    "        for tt in wedgeList[nW]:\n",
    "            HDtractList[t].append(tt)  #building the list of tracts in the Home District  (we'll keep up with below changes)\n",
    "            totPop += hdCPpop[tt]\n",
    "    offsetPop = totPop - aDP  #we have solved for all wedge radii to get each within one tractPop of target ... \n",
    "    HDvPop[t] = totPop\n",
    "    #if offsetPop > 0:   #... now include a splitPoly to nail the avg District Pop\n",
    "    #    isOver = True  #we slightly overshot.  ID the farthest-away tract for split-jettison\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], hdCPpop, neighborList)\n",
    "    #    HDvPop[t] = totPop - (1. - splitTractUse[t]) * tractPop[splitTractNo[t]]\n",
    "    #if offsetPop < 0:\n",
    "    #    isOver = False  #we have undershot.  Pick up the nearest contiguous tract that can be split to fill the gap\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], tractPop, neighborList)\n",
    "    #    HDtractList[t].append(splitTractNo[t])\n",
    "    #    HDvPop[t] = totPop + splitTractUse[t]*tractPop[splitTractNo[t]]       \n",
    "    #if abs(1. - HDvPop[t]/aDP) > 0.005 :  #something went wrong with pop assignation for this HD\n",
    "    #    print(\"WARNING! Total Home District pop was\",HDvPop[t],\"vs target\",aDP,\"for tract\",t)   ##### hdCP topology not yet known; skip partial\n",
    "        \n",
    "    HDPoly1[t] = dummyPoly  #tractGeom[t] #skip constructing the HD poly shape.  Do compute area of the Home District, including final partial\n",
    "    HDarea[t] = 0.\n",
    "    for tt in HDtractList[t]:\n",
    "        if tt == splitTractNo[t]:\n",
    "            tractUse[tt] += nDistricts * HDweight[t] * splitTractUse[t] \n",
    "            #HDarea[t]   += tractArea[tt] * splitTractUse[t]  #these are original (nonsquished) areas\n",
    "            \n",
    "        else:\n",
    "            tractUse[tt] += nDistricts * HDweight[t]\n",
    "            #HDarea[t]    += tractArea[tt]\n",
    "            #HDPoly1[t] = HDPoly1[t].union(tractGeom[tt])\n",
    "    HDPoly1[t] = buildPoly(hdCP[t],HDradius[t], HDangle[t] )\n",
    "    #HDarea[t] = HDPoly1[t].area + splitTractUse[t] * tractArea[splitTractNo[t]]  \n",
    "    #if splitTractUse[t] > 0.5:\n",
    "    #    HDPoly1[t] = HDPoly1[t].union(tractGeom[splitTractNo[t]])\n",
    "                          \n",
    "    #ALL DONE ESTABLISHING THE FINAL HDTRACTLIST.  FINALIZE STATS\n",
    "totalTime = time.time() - codeStartTime\n",
    "print(r3(totalTime),\"seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of vtd usage.\")\n",
    "n_bins=50\n",
    "popTractUse, plotWts = [tractUse[t] for t in populatedTractList], [HDweight[t] for t in populatedTractList]\n",
    "\n",
    "origHDuseAvg, origHDuseSD = getWeightedAvgAndSD(tractUse,HDweight)\n",
    "print(\"vtd avg and sd usage are\",r5(origHDuseAvg), r5(origHDuseSD) )\n",
    "\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "ax.hist(popTractUse, bins=n_bins, weights=plotWts)\n",
    "plt.show()\n",
    "HDvPop = [np.sum([hdCPpop[tt] for tt in HDtractList[t]]) for t in range(nHDs)]\n",
    "plt.scatter([hdCPpop[t] for t in populatedTractList],[HDvPop[t] for t in populatedTractList] )\n",
    "print(\"here are your HD pops\")\n",
    "plt.show()\n",
    "origHDHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #save for posterity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b7f36a0f-a3c6-4b07-aecd-9e010b02918f",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATE = \"25May\"  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "37c238bb-b340-431b-ad11-95e4409d9699",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we will write the polygon-based results to a file\n"
     ]
    }
   ],
   "source": [
    "#last block of option 2 (i.e. generating polygonal HDs from scratch)\n",
    "print(\"Now we will write the polygon-based results to a file\")\n",
    "tractNo = [t for t in range(nHDs) ]\n",
    "HDangle0 = [HDangle[t][0] for t in range(nHDs) ]\n",
    "HDangle1 = [HDangle[t][1] for t in range(nHDs) ]\n",
    "HDangle2 = [HDangle[t][2] for t in range(nHDs) ]\n",
    "HDangle3 = [HDangle[t][3] for t in range(nHDs) ]\n",
    "HDradius0 = [HDradius[t][0] for t in range(nHDs)]\n",
    "HDradius1 = [HDradius[t][1] for t in range(nHDs)]\n",
    "HDradius2 = [HDradius[t][2] for t in range(nHDs)]\n",
    "HDradius3 = [HDradius[t][3] for t in range(nHDs)]\n",
    "hdCPx, hdCPy =   [hdCP[t].x for t in range(nHDs)], [hdCP[t].y for t in range(nHDs)]\n",
    "\n",
    "paramList = [\"STATE\",\"statePop\",\"nDistricts\",\"nHDs\",\"nWedges\",\"popn-toler\",\"levelL\", \"maxAngleRatio\",\n",
    "             \"maxAngle\",\"minAdjRatio\",\"maxUncap\", \"oppWt\", \"calc sec\",\"xScale\",\"outputs...\"]\n",
    "paramValues = [STATE,statePop, nDistricts, nHDs, nWedges, tolerPop, levelL, maxAngleRatio, maxAngle, minAdjRatio, maxUncap,\n",
    "               oppWt, totalTime, xScale, -777]\n",
    "for i in range(nHDs-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "HDdf = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"countyNo\":HDcountyNo,\n",
    "                    \"tractNo\":tractNo,\"centroid x\":hdCPx,\"centroid y\":hdCPy,\"tractPop\":hdCPpop,\n",
    "                    \"HDweight\":HDweight,\"HD-pop\":HDvPop,\"HDarea\":HDarea, \"countyNo\":countyNo,\n",
    "                    \"startAngle\":angle0,\"HDangle0\":HDangle0,\"HDangle1\":HDangle1,\"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\n",
    "                    \"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\"HDradius2\":HDradius2, \"HDradius3\":HDradius3,\"tractUse\":tractUse,\n",
    "                    \"splitTractNo\":splitTractNo,\"splitTractUse\":splitTractUse,\"HDtractList\":HDtractList} ) \n",
    "\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Bnopatch\"+str(int(nDistricts))+\"nW4.csv\"  #solveWedgeB\n",
    "outname = STATE+str(int(nHDs))+\"convexHD2_\"+str(int(nDistricts))+\"nW4_\"+DATE+\".csv\"  #solveWedgeC\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Buutpatch\"+str(int(nDistricts))+\"nW4.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "HDdf.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "075013a3-f3ee-4a44-ac4a-27a7492e027a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./2024state_HD_output/MO4604convexHD2_8nW4_25May.csv\n"
     ]
    }
   ],
   "source": [
    "print(\"./\"+outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6e7e8ccd-8e11-4d6d-970d-b059401e0a3b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the original HD polygon shape assignment csv, e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv ./2024state_HD_output/MO4604convexHD2_8nW4_25May.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have read back in the original HD shapes\n",
      "Ready to capture unit centroids based on these HD poly shapes; see next block\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 --> 1 - we already have an ensemble of HDpoly's and their weights (from running option 2 above)\n",
    "#read them in\n",
    "#determine each cluster's required area fraction capture to get 1.00 cluster use across the HD set\n",
    "#then determine total pop (including cluster in/out) for each HD, and total unit use\n",
    "#then move into triage\n",
    "infilename = input(\"enter the original HD polygon shape assignment csv, \"+ \n",
    "                   \"e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv\")\n",
    "shapeDF = pd.read_csv(infilename)\n",
    "HDvPop = shapeDF[\"HD-pop\"].to_list()\n",
    "HDweight = shapeDF['HDweight'].to_list() #shapeDF['HDwt'].to_list() or #shapeDF['HDweight'].to_list()\n",
    "HDarea = shapeDF['HDarea'].to_list()\n",
    "#tractPop = shapeDF['tractPop'].to_list()   #we will use vtd-mapped pops instead\n",
    "nHDs = len(shapeDF)\n",
    "hdCPx = shapeDF['centroid x'].to_list()   #note: these may be translated from outcroppings into a convex representation of the map\n",
    "hdCPy = shapeDF['centroid y'].to_list()\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs) ]\n",
    "HDradius0 = shapeDF['HDradius0'].to_list()\n",
    "HDradius1 = shapeDF['HDradius1'].to_list()\n",
    "HDradius2 = shapeDF['HDradius2'].to_list()\n",
    "HDradius3 = shapeDF['HDradius3'].to_list()\n",
    "HDangle0 = shapeDF['HDangle0'].to_list()\n",
    "HDangle1 = shapeDF['HDangle1'].to_list()\n",
    "HDangle2 = shapeDF['HDangle2'].to_list()\n",
    "HDangle3 = shapeDF['HDangle3'].to_list()\n",
    "HDradius, HDangle = list(), list()\n",
    "for t in range(nHDs):\n",
    "    HDradius.append( [HDradius0[t],HDradius1[t],HDradius2[t],HDradius3[t] ] )\n",
    "    HDangle.append(  [HDangle0[t], HDangle1[t], HDangle2[t], HDangle3[t]  ] )\n",
    "print(\"I have read back in the original HD shapes\")\n",
    "popHDlist = list()\n",
    "HDpoly = [dummyPoly for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001:\n",
    "        popHDlist.append(t)\n",
    "        HDpoly[t] = buildArcPoly(hdCP[t],HDradius[t], HDangle[t], xScale)\n",
    "if \"countyNo\" in shapeDF.columns.values:  #\"pigs\" == \"can fly\":\n",
    "    HDcountyNo = shapeDF['countyNo'].to_list()\n",
    "else:\n",
    "    print(\"County numbers not in input file. Now I will assign each HD center to a county based on the county geoms\")\n",
    "    HDcountyNo = [-999]*nHDs\n",
    "    for t in popHDlist:\n",
    "        for c, geom in enumerate(countyGeom):\n",
    "            if geom.contains(hdCP[t]):\n",
    "                HDcountyNo[t] = c\n",
    "                break\n",
    "    unassignedList = list()\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] == -999:\n",
    "            unassignedList.append(t)\n",
    "    if len(unassignedList) > 0:\n",
    "        print(\"I found\",len(unassignedList),\"populd HDs whose centers fall outside vtd-based county lines.  Assign to closest county\")\n",
    "        for t in unassignedList:\n",
    "            minDist = maxD\n",
    "            for c in range(nCounties):\n",
    "                dist = countyGeom[c].distance(hdCP[t])\n",
    "                if dist < minDist:\n",
    "                    minDist = dist\n",
    "                    HDcountyNo[t] = c\n",
    "    if len(unassignedList) > 0:\n",
    "        if len(unassignedList) > 20:\n",
    "            print(\"  (too many to plot individually)\")\n",
    "        else:\n",
    "            print(\"FYI, here are those manually assigned HDcenters to their counties\")\n",
    "            for t in unassignedList:\n",
    "                print(t,HDweight[t],\" is the HD row and its weight in the ensemble\")\n",
    "                plotPoly(hdCP[t].buffer(0.1))\n",
    "                plotCenter(int(HDvPop[t]),hdCP[t],6)\n",
    "                plotPoly(countyGeom[HDcountyNo[t]])\n",
    "                plotPoly(MAP,0.2)\n",
    "                plt.show()\n",
    "print(\"Ready to capture unit centroids based on these HD poly shapes; see next block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3f0f8434-5f7a-41d1-bd86-07ec230b4aef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now, let me renormalize the HDweights if there were cut districts\n",
      "our HDweight sum was 0.9999999999998521 but is now 0.9999999999999998\n"
     ]
    }
   ],
   "source": [
    "print(\"now, let me renormalize the HDweights if there were cut districts\")\n",
    "sumWt = np.sum(HDweight)\n",
    "HDweight = [HDweight[t]/sumWt for t in range(nHDs) ]\n",
    "\n",
    "print(\"our HDweight sum was\",sumWt,\"but is now\",np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1422e757-ae8f-4b0e-99b1-b547d90eb6d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af22eba0-8e05-43e7-b96b-6aedab016957",
   "metadata": {},
   "outputs": [],
   "source": [
    "#see early states for continuing w/option 1.  NC and later use option 3 ....\n",
    "# ****if this is a cold restart, would need to read in unit topology before below block  **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8d5f678f-c2df-4ee7-bafe-fd75ba2cb42e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lead = 'option3' - create HDunitLists based on each HD's tractList, with in-out for multiVTD units\n"
     ]
    }
   ],
   "source": [
    "#OPTION 3 \n",
    "print(\"lead = 'option3' - create HDunitLists based on each HD's tractList, with in-out for multiVTD units\")\n",
    "HDtractListString = shapeDF[\"HDtractList\"]\n",
    "HDtractList = [ast.literal_eval(HDtractListString[t]) for t in range(nHDs)]\n",
    "nCCBs = len(CCBlist)\n",
    "CCBpopFrac = [[0. for j in range(nCCBs) ]  for t in range(nHDs)]\n",
    "\n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ec034144-b3dc-4b72-9350-6d85cabcb8a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option3, now assigning each HD's units (excluding CCB's) based on the captured whole vtd's\n",
      "working on vtd --> unit conversion for HD 1 last HD had pop 600765.855\n",
      "working on vtd --> unit conversion for HD 1001 last HD had pop 660036.0\n",
      "working on vtd --> unit conversion for HD 2001 last HD had pop 608790.0\n",
      "working on vtd --> unit conversion for HD 3001 last HD had pop 768949.0\n",
      "working on vtd --> unit conversion for HD 4001 last HD had pop 772886.003\n"
     ]
    }
   ],
   "source": [
    "print(\"Continuing option3, now assigning each HD's units (excluding CCB's) based on the captured whole vtd's\" )\n",
    "HDunitList = [ list() for t in range(nHDs) ]\n",
    "for t in range(nHDs):\n",
    "    if t %1000 == 1:\n",
    "        print(\"working on vtd --> unit conversion for HD\",t,\"last HD had pop\",r3( np.sum([unitPop[u] for u in HDunitList[t-1]]) ) )\n",
    "    capturedCountyPop = [0. for c in range(nCounties)]\n",
    "    capturedCCBpop = [0. for i in range(nCCBs)]\n",
    "    for v in HDtractList[t]:\n",
    "        c = HDcountyNo[v]\n",
    "        if c in range(nCounties):  #we assigned countyno = -999 to unpopulated tracts\n",
    "            if c in unitCounties:\n",
    "                capturedCountyPop[c] += tractPop[v]\n",
    "            elif c in allFusedCounties:\n",
    "                for i, L in enumerate(CCBlist):\n",
    "                    if c in L:\n",
    "                        capturedCCBpop[i] += tractPop[v]\n",
    "            else:  #this vtd not part of a unit or fused county.  Could be split into fragments\n",
    "                for u in countyUnitList[c]:\n",
    "                    if unitParentVTDno[u] == v :  #we assign all units = vtd fragments from this HDTL-captured vtd\n",
    "                        HDunitList[t].append(u)\n",
    "    for c in unitCounties:\n",
    "        if capturedCountyPop[c] > 0.5 * countyPop[c]:\n",
    "            HDunitList[t].append(allUnits.index(c+0.5))\n",
    "    for i in range(nCCBs):  #don't yet assign in/out CCBs.  We'll do so in below block.  Just update max possible use for each CCB\n",
    "        CCBpopFrac[t][i] = capturedCCBpop[i] / CCBpop[i]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1494814c-7b90-425c-b8b1-3a2006bdeb2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option 3.  Determine each CCB's use if we add it to all adjoining HDs\n",
      "CCBno 0 with pop 161018.0 would be used 1.841 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n",
      "CCBno 1 with pop 185562.0 would be used 1.954 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n",
      "CCBno 2 with pop 139686.0 would be used 1.518 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n",
      "CCBno 3 with pop 25126.0 would be used 1.38 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if we are not allowed to use multiple CCB's in a single HD 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CCBno 0 with pop 161018.0 would be used 1.8413465795536117 if we added it to all adjoining districts with no 2-CCB districts\n",
      "CCBno 1 with pop 185562.0 would be used 1.9535691243727018 if we added it to all adjoining districts with no 2-CCB districts\n",
      "CCBno 2 with pop 139686.0 would be used 1.518221297360354 if we added it to all adjoining districts with no 2-CCB districts\n",
      "CCBno 3 with pop 25126.0 would be used 1.3795743335445978 if we added it to all adjoining districts with no 2-CCB districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Continuing option 3.  Determine each CCB's use if we add it to all adjoining HDs\")\n",
    "CCBwouldUse = [0. for i in range(nCCBs)]\n",
    "CCBaddCandidates = [list() for i in range(nCCBs)]\n",
    "for j in range(nCCBs):\n",
    "    jNo = allUnits.index(j+0.25)\n",
    "    ccbNbrSet = set(unitNbrs[jNo])\n",
    "    withCCBpopRat = list()\n",
    "    for t in range(nHDs):\n",
    "        if len(ccbNbrSet.intersection(set(HDunitList[t]))) > 0:\n",
    "            CCBwouldUse[j] += HDweight[t] * nDistricts\n",
    "            CCBaddCandidates[j].append(t)\n",
    "            withCCBpopRat.append((np.sum([unitPop[u] for u in HDunitList[t]]) + CCBpop[j]) / aDP )\n",
    "    print(\"CCBno\",j,\"with pop\",CCBpop[j],\"would be used\",r3(CCBwouldUse[j]),\"if we added it to all adjoining districts\")\n",
    "    print(\"Here is the histogram of relative district pop with these added\")\n",
    "    plt.hist(withCCBpopRat,histtype=\"step\",label=\"CCB \"+str(j))\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "avoidMultiCCBuse = int(input(\"enter 1 if we are not allowed to use multiple CCB's in a single HD\"))  #option by state\n",
    "if avoidMultiCCBuse == 1:\n",
    "    for j in range(nCCBs):  #this block -- preventing HDs from picking up multiple CCB's; instead, pick up the closest one  \n",
    "        jNo = allUnits.index(j+0.25)\n",
    "        for jj in range(j+1, nCCBs):\n",
    "            jjNo = allUnits.index(j+0.25)\n",
    "            for t in CCBaddCandidates[j].copy():\n",
    "                if t in CCBaddCandidates[j]:\n",
    "                    if t in CCBaddCandidates[jj].copy():\n",
    "                        dist_j, dist_jj = hdCP[t].distance(unitCP[jNo]), hdCP[t].distance(unitCP[jjNo])\n",
    "                        if dist_j > dist_jj:\n",
    "                            CCBaddCandidates[j].remove(t)  #for MN, don't allow any districts with two CCB's\n",
    "                            CCBwouldUse[j] -= HDweight[t] * nDistricts\n",
    "                        else:\n",
    "                            CCBaddCandidates[jj].remove(t)\n",
    "                            CCBwouldUse[jj] -= HDweight[t] * nDistricts\n",
    "for j in range(nCCBs):    \n",
    "    print(\"CCBno\",j,\"with pop\",CCBpop[j],\"would be used\",CCBwouldUse[j],\"if we added it to all adjoining districts with no 2-CCB districts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0d38eb0e-b516-4fd4-bb9e-6ca0eecfa772",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option 3. Now assign CCB units.  For each, work from most to least underpopped\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter target CCBuse for CCB 0 I reco 1.02, as we may lose a little later 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 0 is 1.02125 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter target CCBuse for CCB 1 I reco 1.02, as we may lose a little later 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 1 is 1.02073 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter target CCBuse for CCB 2 I reco 1.02, as we may lose a little later 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 2 is 1.02133 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter target CCBuse for CCB 3 I reco 1.02, as we may lose a little later 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 3 is 1.02352 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Continuing option 3. Now assign CCB units.  For each, work from most to least underpopped\")\n",
    "#previously, we went from most-used to least-used CCB vtds until each has unit use\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "CCBuse = [0. for i in range(nCCBs)]\n",
    "for j in range(nCCBs):\n",
    "    minCCBuse = float(input(\"enter target CCBuse for CCB \"+str(j)+\" I reco 1.02, as we may lose a little later\"))\n",
    "    postCCBaddPops = list()\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    #ccbNbrSet = set(unitNbrs[unitNo])\n",
    "    #ccbFrac = [-1.*CCBpopFrac[t][j] for t in range(nHDs) ]  # -1 to go from most- to least-used\n",
    "    #idx = np.argsort(ccbFrac)\n",
    "    preCCBpop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in CCBaddCandidates[j]]\n",
    "    idx = np.argsort(preCCBpop)\n",
    "    i = 0\n",
    "    t = CCBaddCandidates[j][idx[i]] #idx[i]\n",
    "    while CCBuse[j] < minCCBuse  and i < len(CCBaddCandidates[j]) :  #and CCBpopFrac[t][j] > 0:\n",
    "        t = CCBaddCandidates[j][idx[i]] #idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        HDunitList[t].append(unitNo)\n",
    "        postCCBaddPops.append(np.sum([unitPop[u] for u in HDunitList[t] ]))\n",
    "        i +=1\n",
    "    unitUse[unitNo] = CCBuse[j]\n",
    "    print(\"final unit use of CCB\",j, \"is\",r5(CCBuse[j]),\"; here is a histogram of HDpop using this CCB\")\n",
    "    plt.hist(postCCBaddPops)\n",
    "    plt.axvline(aDP,ls=\"--\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf747d9c-cf6d-420e-9c3c-6723619586ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#end of preliminaries for \"option 3\" - converting HDtractlists to unit lists instead of option 2's geometric probing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26102ca2-79c1-48db-b873-7fa3a1553911",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "dc26d08c-a5d9-4e7e-9da5-ae98cf658a3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the histogram of HD pops relative to aDP\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "orig unit avg and sd usage are 1.01482 0.12872\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs) ]\n",
    "print(\"Here is the histogram of HD pops relative to aDP\")\n",
    "plt.hist([HDvPop[t] / aDP for t in range(nHDs) ], bins=20, weights = HDweight, label= \"HDpop / target\" )\n",
    "plt.show()\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53e4c298-e6cb-4fee-a511-b8f6c3940539",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c4183011-724a-4014-8c94-f9f9382c7e75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Did I grossly overuse any units, or skip any live ones?\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "above: underused < 0.5; below overused > 1.6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Did I grossly overuse any units, or skip any live ones?\")\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < 0.5 and unitPop[u] > 5:\n",
    "        plotPoly(unitGeom[u])\n",
    "        #print(u,unitPop[u], unitUse[u])\n",
    "        #for c in range(nCounties):\n",
    "        #    if u in countyUnitList[c]:\n",
    "        #        print(u,\"is in county\",c)\n",
    "        #        print(\"its neighbor units are\",unitNbrs[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"above: underused < 0.5; below overused > 1.6\")\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 1.6:\n",
    "        plotPoly(unitGeom[u],0.3)\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ce9292b0-7d6a-419a-9491-d0def779ad04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 4604 populated HDs out of 4604\n"
     ]
    }
   ],
   "source": [
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if len(HDunitList[t]) > 0:\n",
    "        popHDlist.append(t)\n",
    "populatedTractList = popHDlist.copy()\n",
    "print(\"there are a total of\",len(populatedTractList),\"populated HDs out of\",nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f7f36559-7f1d-4203-8b24-0e8cc2ce1753",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this will show if we had any duplicated units in HDunitLists\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this will show if we had any duplicated units in HDunitLists\")\n",
    "excess = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    excess[t] = len(HDunitList[t]) - len(set(HDunitList[t]))\n",
    "plt.hist(excess)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "27bc7f0a-d7ff-4c68-830f-29ba5013c314",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#popHDlist = populatedTractList.copy()  #somehow our popHDlist started to include the cut District HD starters\n",
    "HDunitList = [list(set(HDunitList[t])) for t in range(nHDs)]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "af8a4945-40c2-40df-8dd4-d516f4de2f0f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 54\n",
      "working on HD 1400 time is now 112\n",
      "working on HD 2100 time is now 179\n",
      "working on HD 2800 time is now 232\n",
      "working on HD 3500 time is now 329\n",
      "working on HD 4200 time is now 431\n",
      "out of 4604 total HDs, there were 2768.0 contiguous and 888.0 complement-contiguous HDs\n",
      "1562 HDs had both discontiguity problems, while enclave-only = 2154 and discontig only= 274\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 1836 2154\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "40f06e44-45be-4a36-b62c-26698af70e06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 730895 district pop vs 769364 target\n",
      "we'll also trim any HD with total islands' pop <= 15387\n",
      "working on HD 5\n",
      "working on HD 501\n",
      "working on HD 1079\n",
      "working on HD 1406\n",
      "working on HD 1903\n",
      "working on HD 2534\n",
      "working on HD 3079\n",
      "working on HD 3593\n",
      "working on HD 4114\n",
      "working on HD 4522\n",
      "Out of 1836 discontig HDs 1836 had discontig HDs.\n",
      "Of these, 1445 won't be under 730895 after all minor discontigys were shed, while 391 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "reclassifying HD 5\n",
      "reclassifying HD 1265\n",
      "reclassifying HD 2534\n",
      "reclassifying HD 3836\n",
      "There are 391 HDs still with both discontinuity problems\n",
      "Additionally, 1231 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%200 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for i, t in enumerate(sPgenerator):    \n",
    "    if i%500 == 0:\n",
    "        print(\"reclassifying HD\",t)\n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4037f8cb-661b-4032-bfb9-e147975e3bbb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classify further: ID those with adjoiners intersecting the map boundary vs internal islands\n",
      "working on enclave-generating HD 2 0 seconds elapsed\n",
      "working on enclave-generating HD 1479 2 seconds elapsed\n",
      "working on enclave-generating HD 2430 5 seconds elapsed\n",
      "working on enclave-generating HD 3332 10 seconds elapsed\n",
      "working on enclave-generating HD 4213 19 seconds elapsed\n",
      "working on enclave-generating HD 1241 22 seconds elapsed\n",
      "working on enclave-generating HD 3238 26 seconds elapsed\n",
      "Out of 3385 HDpolys that generated enclaves, 0 had contiguous adjrs, while 3302 neighbored a state boundary while 83 had only internal enclaves\n"
     ]
    }
   ],
   "source": [
    "print(\"Classify further: ID those with adjoiners intersecting the map boundary vs internal islands\")\n",
    "internals, edgers, nOK = list(), list(), 0\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(eLgenerator):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on enclave-generating HD\",t,int(time.time()-startTime),\"seconds elapsed\")\n",
    "    twoNbrs = get2nbrs(HDunitList[t],unitNbrs)\n",
    "    isContig, adjSublist = isContiguous(twoNbrs,unitNbrs)\n",
    "    if isContig:\n",
    "        nOK +=1\n",
    "    else:\n",
    "        if len (set(getAdjoiners(HDunitList[t],unitNbrs)).intersection(set(borderUnits)))  > 0:\n",
    "            edgers.append(t)\n",
    "        else:\n",
    "            internals.append(t)\n",
    "print(\"Out of\",len(eLgenerator),\"HDpolys that generated enclaves,\",nOK,\"had contiguous adjrs, while\",len(edgers),\n",
    "      \"neighbored a state boundary while\",len(internals),\"had only internal enclaves\")   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "05f929ce-4f94-47a6-baf4-60c5a89e2ae5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 807832 district pop vs 769364 target\n",
      "working on enclave-y HD 199 . We have evaluated 0 of 3385 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 377 . We have evaluated 200 of 3385 enclavy HDs.Time is now 27\n",
      "working on enclave-y HD 943 . We have evaluated 400 of 3385 enclavy HDs.Time is now 47\n",
      "working on enclave-y HD 1533 . We have evaluated 600 of 3385 enclavy HDs.Time is now 67\n",
      "working on enclave-y HD 1973 . We have evaluated 800 of 3385 enclavy HDs.Time is now 87\n",
      "working on enclave-y HD 2347 . We have evaluated 1000 of 3385 enclavy HDs.Time is now 110\n",
      "working on enclave-y HD 2681 . We have evaluated 1200 of 3385 enclavy HDs.Time is now 126\n",
      "working on enclave-y HD 3066 . We have evaluated 1400 of 3385 enclavy HDs.Time is now 150\n",
      "working on enclave-y HD 3437 . We have evaluated 1600 of 3385 enclavy HDs.Time is now 183\n",
      "working on enclave-y HD 3800 . We have evaluated 1800 of 3385 enclavy HDs.Time is now 219\n",
      "working on enclave-y HD 4167 . We have evaluated 2000 of 3385 enclavy HDs.Time is now 252\n",
      "working on enclave-y HD 112 . We have evaluated 2200 of 3385 enclavy HDs.Time is now 286\n",
      "working on enclave-y HD 771 . We have evaluated 2400 of 3385 enclavy HDs.Time is now 310\n",
      "working on enclave-y HD 1673 . We have evaluated 2600 of 3385 enclavy HDs.Time is now 335\n",
      "working on enclave-y HD 2344 . We have evaluated 2800 of 3385 enclavy HDs.Time is now 361\n",
      "working on enclave-y HD 3198 . We have evaluated 3000 of 3385 enclavy HDs.Time is now 386\n",
      "working on enclave-y HD 4031 . We have evaluated 3200 of 3385 enclavy HDs.Time is now 420\n",
      "Out of 3385 HDs with enclaves 3385 had enclaves.\n",
      "Of these, 3026 wouldn't be over 807832 if all enclaves filled, while 359 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(internals + edgers):\n",
    "    if iii%200 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(internals+edgers),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(internals+edgers),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8987ad1f-d372-415d-a2ec-8fc6736c5102",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83 3302 1562 1204\n",
      "359 359 3026\n"
     ]
    }
   ],
   "source": [
    "print(len(internals),len(edgers),len(doubleTroubleList),len(set(edgers).intersection(set(doubleTroubleList))))\n",
    "print(len(set(edgers).difference(set(canFill))) ,len(cantFill) , len(canFill))\n",
    "#610 3379 2187 1954\n",
    "#80 80 3909  for original MN option3 (assigned CCBs by captured area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8fa887be-92e6-422a-8e80-e00fd3265aab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.01639 0.12831\n",
      "CCB cluster 0 = unit 4019 with pop 161018.0 now has use of 1.021249853572269\n",
      "CCB cluster 1 = unit 4020 with pop 185562.0 now has use of 1.0207273441558238\n",
      "CCB cluster 2 = unit 4021 with pop 139686.0 now has use of 1.0213330391510074\n",
      "CCB cluster 3 = unit 4022 with pop 25126.0 now has use of 1.0228134824976447\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e9d50ef8-39b2-4e22-a11e-cabeda78721e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b759b3c7-b8a3-4d2c-a8fe-50fa93b3e940",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping\n",
    "HDunitList = [savedHDunitList[t].copy() for t in range(nHDs) ]   #restart\n",
    "HDvPop = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.axvline(aDP, ls=\"--\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "551454fb-d57c-466f-8b31-39ccb2a728de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the HD centers for 359 cantFill HDs with border or big enclaves\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the HD centers for\",len(cantFill),\"cantFill HDs with border or big enclaves\")\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "for t in cantFill:\n",
    "    plotPoly(hdCP[t].buffer(0.02))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5269a224-4a97-417c-99af-6a98df0087ae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the barredJettisonSet (cannot be shed in below MUSTFREE code) is {4019, 4020, 4021, 4022}\n",
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 800\n",
      "working on avg unit-to-HDcp dist for HD 1600\n",
      "working on avg unit-to-HDcp dist for HD 2400\n",
      "working on avg unit-to-HDcp dist for HD 3200\n",
      "working on avg unit-to-HDcp dist for HD 4000\n",
      "all unit-toHDcp distances computed\n"
     ]
    }
   ],
   "source": [
    "barredJettisonSet = set(list())\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u]% 1 > 0.23 and allUnits[u]%1 < 0.27:\n",
    "        barredJettisonSet.add(u)\n",
    "print(\"the barredJettisonSet (cannot be shed in below MUSTFREE code) is\",barredJettisonSet)\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "print(\"all unit-toHDcp distances computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6e2e44bd-a084-42f6-b533-5dc6d21912a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this code will solve large enclaves so HD and complement are contiguous for 359 HDs\n",
      "working on HD 22 cantFill 0 of 359 .Time is now 0\n",
      "working on HD 420 cantFill 20 of 359 .Time is now 2\n",
      "working on HD 639 cantFill 40 of 359 .Time is now 2\n",
      "working on HD 881 cantFill 60 of 359 .Time is now 2\n",
      "working on HD 1104 cantFill 80 of 359 .Time is now 3\n",
      "working on HD 1532 cantFill 100 of 359 .Time is now 3\n",
      "working on HD 1727 cantFill 120 of 359 .Time is now 3\n",
      "working on HD 1907 cantFill 140 of 359 .Time is now 3\n",
      "working on HD 2065 cantFill 160 of 359 .Time is now 4\n",
      "working on HD 2581 cantFill 180 of 359 .Time is now 4\n",
      "working on HD 4380 cantFill 200 of 359 .Time is now 6\n",
      "working on HD 229 cantFill 220 of 359 .Time is now 7\n",
      "working on HD 588 cantFill 240 of 359 .Time is now 8\n",
      "working on HD 730 cantFill 260 of 359 .Time is now 8\n",
      "working on HD 1078 cantFill 280 of 359 .Time is now 8\n",
      "working on HD 1693 cantFill 300 of 359 .Time is now 9\n",
      "working on HD 2048 cantFill 320 of 359 .Time is now 10\n",
      "working on HD 4359 cantFill 340 of 359 .Time is now 11\n",
      "after the MustFree code run, we have the following for cluster usage\n",
      "current avg use and its sd are 1.0154 0.1256\n",
      "CCB cluster 0 = unit 4019 with pop 161018.0 now has use of 1.021249853572269\n",
      "CCB cluster 1 = unit 4020 with pop 185562.0 now has use of 1.0196069383921682\n",
      "CCB cluster 2 = unit 4021 with pop 139686.0 now has use of 1.0184644364591422\n",
      "CCB cluster 3 = unit 4022 with pop 25126.0 now has use of 1.1057871979668918\n"
     ]
    },
    {
     "data": {
      "image/png": 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aEwAADG9+BxZJKi8vV3l5eZ+fnQkhZ6Snp8vr9X6jMWGWluOdOubqDvq440ZFK3VsXNDHBQAMfgEFFgxfLcc7ZVmzU5097qCPHTcyStsXFxJaAADnILDAL8dc3erscctekq3JSaODNu7BtpOqqGvWMVc3gQUAcA4CCwIyOWm0MlMTwl0GAGCYGLQ3jgMAAMMHgQUAABiPU0LAN3Sw7eSgGBMABjMCCxCgcaOiFTcyShV1zSEZP25klMaNig7J2AAw2BBYgACljo3T9sWFIbknjcR9aQDgzxFYgG8gdWwcoQIABgCBBUZhPQgAoC8EFhiB9SAAgPMhsMAIrAcBAJwPgQXGYD0IAKA/3DgOAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjDci3AUgNFqOd+qYqzvo4x5sOxn0MQEAuBACyxDUcrxTljU71dnjDsn4cSOjNG5UdEjGBgCgLwSWIeiYq1udPW7ZS7I1OWl00McfNypaqWPjgj4uAAD9CWgNS3V1tdLT0xUbG6v8/Hzt3r37vP03bdqkKVOmKDY2VjfccIO2bt3a6/OTJ0+qvLxcV1xxheLi4jR16lTV1NQEUhr+zOSk0cpMTQj6i7ACABhofgeWuro6Wa1WVVVVqampSVlZWSoqKlJbW1uf/Xft2qU5c+bo4Ycf1t69e1VcXKzi4mLt27fP18dqtaq+vl7//u//ro8++kgVFRUqLy/XG2+8EfieAQCAIcPvwLJ27VrNmzdPZWVlviMhl1xyiV566aU++z/33HO6/fbb9fjjj+u6667TU089pRtvvFHr16/39dm1a5dKS0t16623Kj09XT/+8Y+VlZV1wSM3AABgePArsHR3d6uxsVEWi+XsAJGRslgsamho6HObhoaGXv0lqaioqFf/GTNm6I033lBLS4u8Xq/efvttffLJJ/re977Xby1dXV3q6Ojo9QIAAEOTX4Hl6NGjcrvdSk5O7tWenJwsp9PZ5zZOp/OC/detW6epU6fqiiuuUHR0tG6//XZVV1frlltu6bcWm82mhIQE3ystLc2fXQEAAIOIETeOW7dund5//3298cYbamxs1Jo1a7RgwQJt3769320qKyvV3t7uex0+fHgAKwYAAAPJr8uaExMTFRUVpdbW1l7tra2tSklJ6XOblJSU8/bv7OzU0qVL9frrr2vWrFmSpG9961tqbm7W6tWrzzmddEZMTIxiYmL8KR8AAAxSfh1hiY6O1vTp0+VwOHxtHo9HDodDBQUFfW5TUFDQq78kbdu2zde/p6dHPT09iozsXUpUVJQ8Ho8/5QEAgCHK7xvHWa1WlZaWKicnR3l5ebLb7XK5XCorK5MkzZ07V6mpqbLZbJKkRYsWqbCwUGvWrNGsWbO0ceNG7dmzRxs2bJAkxcfHq7CwUI8//rji4uJ01VVXaefOnfr5z3+utWvXBnFXAQDAYOV3YCkpKdGRI0e0YsUKOZ1OZWdnq76+3rew9tChQ72OlsyYMUO1tbVatmyZli5dqoyMDG3evFmZmZm+Phs3blRlZaUeeOAB/elPf9JVV12lf/7nf9b8+fODsIsAAGCwi/B6vd5wFxEMHR0dSkhIUHt7u+Lj48NdTljta2nXneve05aFNyszNSHc5cAw/P0AYJKL/f024iohAACA8yGwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjBRRYqqurlZ6ertjYWOXn52v37t3n7b9p0yZNmTJFsbGxuuGGG7R169Zz+nz00Uf6/ve/r4SEBI0aNUq5ubk6dOhQIOUBAIAhxu/AUldXJ6vVqqqqKjU1NSkrK0tFRUVqa2vrs/+uXbs0Z84cPfzww9q7d6+Ki4tVXFysffv2+fr8z//8j26++WZNmTJFO3bs0H//939r+fLlio2NDXzPAADAkBHh9Xq9/myQn5+v3NxcrV+/XpLk8XiUlpamhQsXasmSJef0Lykpkcvl0pYtW3xtN910k7Kzs1VTUyNJuu+++zRy5Ej94he/CHhHOjo6lJCQoPb2dsXHxwc8zlCwr6Vdd657T1sW3qzM1IRwlwPD8PcDgEku9vfbryMs3d3damxslMViOTtAZKQsFosaGhr63KahoaFXf0kqKiry9fd4PHrzzTd1zTXXqKioSElJScrPz9fmzZvPW0tXV5c6Ojp6vQAAwNDkV2A5evSo3G63kpOTe7UnJyfL6XT2uY3T6Txv/7a2Np08eVKrVq3S7bffrt/+9re6++679cMf/lA7d+7stxabzaaEhATfKy0tzZ9dAQAAg8iIcBfg8XgkST/4wQ/02GOPSZKys7O1a9cu1dTUqLCwsM/tKisrZbVafe87OjoILUCYtRzv1DFXd0jGHjcqWqlj40IyNgDz+RVYEhMTFRUVpdbW1l7tra2tSklJ6XOblJSU8/ZPTEzUiBEjNHXq1F59rrvuOr333nv91hITE6OYmBh/ygcQQi3HO2VZs1OdPe6QjB83MkrbFxcSWoBhyq/AEh0drenTp8vhcKi4uFjS6SMkDodD5eXlfW5TUFAgh8OhiooKX9u2bdtUUFDgGzM3N1cff/xxr+0++eQTXXXVVf6UByCMjrm61dnjlr0kW5OTRgd17INtJ1VR16xjrm4CCzBM+X1KyGq1qrS0VDk5OcrLy5PdbpfL5VJZWZkkae7cuUpNTZXNZpMkLVq0SIWFhVqzZo1mzZqljRs3as+ePdqwYYNvzMcff1wlJSW65ZZbdNttt6m+vl7/+Z//qR07dgRnLwEMmMlJo7n6CEDQ+R1YSkpKdOTIEa1YsUJOp1PZ2dmqr6/3Law9dOiQIiPPruWdMWOGamtrtWzZMi1dulQZGRnavHmzMjMzfX3uvvtu1dTUyGaz6dFHH9W1116rX//617r55puDsIsAAGCwC2jRbXl5eb+ngPo6KjJ79mzNnj37vGM+9NBDeuihhwIpBwAADHE8SwgAABiPwAIAAIxHYAEAAMYjsAAAAOOF/U63AMLjYNtJo8cDgD9HYAGGmXGjohU3MkoVdc1BHztuZJTGjYoO+rgAQGABhpnUsXHavrgwJM/84Xk/AEKFwAIMQ6lj4wgWAAYVFt0CAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPG4NT+AQSMUT4Tm+UfA4EBgAWC8UD9hevviQkILYDgCCwDjheoJ0wfbTqqirlnHXN0EFsBwBBYAgwJPmAaGNxbdAgAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGC8gAJLdXW10tPTFRsbq/z8fO3evfu8/Tdt2qQpU6YoNjZWN9xwg7Zu3dpv3/nz5ysiIkJ2uz2Q0gAAwBDkd2Cpq6uT1WpVVVWVmpqalJWVpaKiIrW1tfXZf9euXZozZ44efvhh7d27V8XFxSouLta+ffvO6fv666/r/fff14QJE/zfEwAAMGT5HVjWrl2refPmqaysTFOnTlVNTY0uueQSvfTSS332f+6553T77bfr8ccf13XXXaennnpKN954o9avX9+rX0tLixYuXKhf/vKXGjlyZGB7AwAAhiS/Akt3d7caGxtlsVjODhAZKYvFooaGhj63aWho6NVfkoqKinr193g8evDBB/X444/r+uuvv6haurq61NHR0esFAACGphH+dD569KjcbreSk5N7tScnJ+vAgQN9buN0Ovvs73Q6fe+feeYZjRgxQo8++uhF12Kz2bRy5Uo/qjdTy/FOHXN1B3XMg20ngzoeAADh5ldgCYXGxkY999xzampqUkRExEVvV1lZKavV6nvf0dGhtLS0UJQYMi3HO2VZs1OdPe6gjx03MkrjRkUHfVwAAMLBr8CSmJioqKgotba29mpvbW1VSkpKn9ukpKSct/+7776rtrY2XXnllb7P3W63Fi9eLLvdri+++KLPcWNiYhQTE+NP+cY55upWZ49b9pJsTU4aHdSxx42KVurYuKCOCQBAuPgVWKKjozV9+nQ5HA4VFxdLOr3+xOFwqLy8vM9tCgoK5HA4VFFR4Wvbtm2bCgoKJEkPPvhgn2tcHnzwQZWVlflT3qA1OWm0MlMTwl0GAADG8vuUkNVqVWlpqXJycpSXlye73S6Xy+ULF3PnzlVqaqpsNpskadGiRSosLNSaNWs0a9Ysbdy4UXv27NGGDRskSePHj9f48eN7fcfIkSOVkpKia6+99pvuHwAAGAL8DiwlJSU6cuSIVqxYIafTqezsbNXX1/sW1h46dEiRkWcvPpoxY4Zqa2u1bNkyLV26VBkZGdq8ebMyMzODtxcAAGBIC2jRbXl5eb+ngHbs2HFO2+zZszV79uyLHr+/dSsAAGB4CvtVQgAwVIXitgUSi+oxPBFYACAEQn3bgu2LCwktGFYILAAQAqG6bcHBtpOqqGvWMVc3gQXDCoEFwLAXirtDnxmT2xYAwUFgATBsjRsVrbiRUaqoaw7J+NxxGggeAguAYSt1bJy2Ly4MycJYicWxQDARWAAMa6lj4wgVwCAQeeEuAAAA4UVgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYLwR4S4AAOC/g20nQzLuuFHRSh0bF5KxgW+CwAIAg8i4UdGKGxmlirrmkIwfNzJK2xcXElpgHAILAAwiqWPjtH1xoY65uoM+9sG2k6qoa9YxVzeBBcYhsADAIJM6Ni6kgSIUp5s41YRvisACAJAU2tNNnGrCNxVQYKmurtazzz4rp9OprKwsrVu3Tnl5ef3237Rpk5YvX64vvvhCGRkZeuaZZ3THHXdIknp6erRs2TJt3bpVn332mRISEmSxWLRq1SpNmDAhsL0CAPgtVKebONWEYPA7sNTV1clqtaqmpkb5+fmy2+0qKirSxx9/rKSkpHP679q1S3PmzJHNZtOdd96p2tpaFRcXq6mpSZmZmTp16pSampq0fPlyZWVl6dixY1q0aJG+//3va8+ePUHZSQDAxQn16SYgUBFer9frzwb5+fnKzc3V+vXrJUkej0dpaWlauHChlixZck7/kpISuVwubdmyxdd20003KTs7WzU1NX1+xwcffKC8vDz98Y9/1JVXXnlRdXV0dCghIUHt7e2Kj4/3Z5fCZl9Lu+5c9562LLxZmakJ4S4HAEKCf+twPhf7++3XjeO6u7vV2Ngoi8VydoDISFksFjU0NPS5TUNDQ6/+klRUVNRvf0lqb29XRESExo4d22+frq4udXR09HoBAIChya/AcvToUbndbiUnJ/dqT05OltPp7HMbp9PpV/+vvvpKf//3f685c+acN2nZbDYlJCT4Xmlpaf7sCgAAGESMujV/T0+P7r33Xnm9Xr3wwgvn7VtZWan29nbf6/DhwwNUJQAAGGh+LbpNTExUVFSUWltbe7W3trYqJSWlz21SUlIuqv+ZsPLHP/5Rb7311gXXocTExCgmJsaf8gEAwCDl1xGW6OhoTZ8+XQ6Hw9fm8XjkcDhUUFDQ5zYFBQW9+kvStm3bevU/E1Y+/fRTbd++XePHj/enLAAAMMT5fVmz1WpVaWmpcnJylJeXJ7vdLpfLpbKyMknS3LlzlZqaKpvNJklatGiRCgsLtWbNGs2aNUsbN27Unj17tGHDBkmnw8pf/dVfqampSVu2bJHb7fatb7n00ksVHR0drH0FAACDlN+BpaSkREeOHNGKFSvkdDqVnZ2t+vp638LaQ4cOKTLy7IGbGTNmqLa2VsuWLdPSpUuVkZGhzZs3KzMzU5LU0tKiN954Q5KUnZ3d67vefvtt3XrrrQHuGgAAGCoCutNteXm5ysvL+/xsx44d57TNnj1bs2fP7rN/enq6/LwVDAAAGGaMukoIAACgLzz8EAAwIELxFGiJJ0EPFwQWAEBIhfIp0BJPgh4uCCwAgJAK1VOgJZ4EPZwQWAAAIcdToPFNsegWAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxuEroIrQc7wzZ5XgAAODCCCwX0HK8U5Y1O9XZ4w7J+HEjozRuFE+kBgDgfAgsF3DM1a3OHrfsJdmanDQ66ONzS2kAAC6MwHKRJieNVmZqQrjLAABgWGLRLQAAMB6BBQAAGI9TQgCAQS8UV12yxtAsBBYAwKA1blS04kZGqaKuOehjx42M0vbFhYQWQxBYAACDVurYOG1fXBj0e2UdbDupirpmHXN1E1gMQWABAAxqqWPjCBXDAIEFAIB+hOqO5KyP8R+BBQCAvxDKtTES62MCQWABAOAvhGptjMT6mEARWAAA6ANrY8zCjeMAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPG40y0AAENIy/HOkDxSINwPbAwosFRXV+vZZ5+V0+lUVlaW1q1bp7y8vH77b9q0ScuXL9cXX3yhjIwMPfPMM7rjjjt8n3u9XlVVVenFF1/U8ePHNXPmTL3wwgvKyMgIpDwAAIwXiidB/5+rW/N/0ajOHnfQxw73Axv9Dix1dXWyWq2qqalRfn6+7Ha7ioqK9PHHHyspKemc/rt27dKcOXNks9l05513qra2VsXFxWpqalJmZqYk6V/+5V/0/PPP62c/+5kmTpyo5cuXq6ioSPv371dsbOw330sAAAwxEE+C/tlDeRo/KjpoY5rwwMYIr9fr9WeD/Px85ebmav369ZIkj8ejtLQ0LVy4UEuWLDmnf0lJiVwul7Zs2eJru+mmm5Sdna2amhp5vV5NmDBBixcv1t/93d9Jktrb25WcnKxXXnlF991330XV1dHRoYSEBLW3tys+Pt6fXTqvfS3tunPde9qy8GZlpiYEbVwAwPAVqtM2UmhO3YTyt/Bif7/9OsLS3d2txsZGVVZW+toiIyNlsVjU0NDQ5zYNDQ2yWq292oqKirR582ZJ0ueffy6n0ymLxeL7PCEhQfn5+WpoaOg3sHR1damrq8v3vr29XdLpHQ+mkyc65Ok6pZMnOtTRERHUsQEAw9OYSGnMmFD9pvSoo6MnqCOG8rfwzO/2hY6f+BVYjh49KrfbreTk5F7tycnJOnDgQJ/bOJ3OPvs7nU7f52fa+uvTF5vNppUrV57TnpaWduEdCUCBPSTDAgAwaITyt/DEiRNKSOj/6M2gvUqosrKy15Ebj8ejP/3pTxo/frwiIsw4EtLR0aG0tDQdPnw4qKepBhvm4Szm4izm4izm4izm4qzhMhder1cnTpzQhAkTztvPr8CSmJioqKgotba29mpvbW1VSkpKn9ukpKSct/+Z/21tbdXll1/eq092dna/tcTExCgmJqZX29ixYy92VwZUfHz8kP7LdrGYh7OYi7OYi7OYi7OYi7OGw1yc78jKGX7dOC46OlrTp0+Xw+HwtXk8HjkcDhUUFPS5TUFBQa/+krRt2zZf/4kTJyolJaVXn46ODv3+97/vd0wAADC8+H1KyGq1qrS0VDk5OcrLy5PdbpfL5VJZWZkkae7cuUpNTZXNZpMkLVq0SIWFhVqzZo1mzZqljRs3as+ePdqwYYMkKSIiQhUVFfqnf/onZWRk+C5rnjBhgoqLi4O3pwAAYNDyO7CUlJToyJEjWrFihZxOp7Kzs1VfX+9bNHvo0CFFRp49cDNjxgzV1tZq2bJlWrp0qTIyMrR582bfPVgk6YknnpDL5dKPf/xjHT9+XDfffLPq6+sH/T1YYmJiVFVVdc6pq+GGeTiLuTiLuTiLuTiLuTiLuejN7/uwAAAADDQefggAAIxHYAEAAMYjsAAAAOMRWAAAgPEILN9AdXW10tPTFRsbq/z8fO3evfu8/e12u6699lrFxcUpLS1Njz32mL766qsBqja0/JmLnp4e/eM//qOuvvpqxcbGKisrS/X19QNYbei88847uuuuuzRhwgRFRET4npl1Pjt27NCNN96omJgYTZ48Wa+88krI6xwI/s7F//7v/+r+++/XNddco8jISFVUVAxInQPB37l47bXX9N3vfleXXXaZ4uPjVVBQoN/85jcDU2wI+TsP7733nmbOnKnx48crLi5OU6ZM0b/+678OTLEhFsi/FWf87ne/04gRI857c9WhiMASoLq6OlmtVlVVVampqUlZWVkqKipSW1tbn/1ra2u1ZMkSVVVV6aOPPtK//du/qa6uTkuXLh3gyoPP37lYtmyZfvrTn2rdunXav3+/5s+fr7vvvlt79+4d4MqDz+VyKSsrS9XV1RfV//PPP9esWbN02223qbm5WRUVFXrkkUeGxI+Tv3PR1dWlyy67TMuWLVNWVlaIqxtY/s7FO++8o+9+97vaunWrGhsbddttt+muu+4a9P+N+DsPo0aNUnl5ud555x199NFHWrZsmZYtW+a7j9dg5u9cnHH8+HHNnTtX3/nOd0JUmcG8CEheXp53wYIFvvdut9s7YcIEr81m67P/ggULvN/+9rd7tVmtVu/MmTNDWudA8HcuLr/8cu/69et7tf3whz/0PvDAAyGtc6BJ8r7++uvn7fPEE094r7/++l5tJSUl3qKiohBWNvAuZi7+XGFhoXfRokUhqyec/J2LM6ZOnepduXJl8AsKk0Dn4e677/b+6Ec/Cn5BYeTPXJSUlHiXLVvmraqq8mZlZYW0LtNwhCUA3d3damxslMVi8bVFRkbKYrGooaGhz21mzJihxsZG36mSzz77TFu3btUdd9wxIDWHSiBz0dXVdc5NAePi4vTee++FtFYTNTQ09Jo7SSoqKup37jA8eTwenThxQpdeemm4SwmrvXv3ateuXSosLAx3KWHx8ssv67PPPlNVVVW4SwmLQfu05nA6evSo3G637+6+ZyQnJ+vAgQN9bnP//ffr6NGjuvnmm+X1evX1119r/vz5g/6UUCBzUVRUpLVr1+qWW27R1VdfLYfDoddee01ut3sgSjaK0+nsc+46OjrU2dmpuLi4MFUGk6xevVonT57UvffeG+5SwuKKK67QkSNH9PXXX+vJJ5/UI488Eu6SBtynn36qJUuW6N1339WIEcPzp5sjLANkx44devrpp/WTn/xETU1Neu211/Tmm2/qqaeeCndpA+65555TRkaGpkyZoujoaJWXl6usrKzXIx0AnFZbW6uVK1fqV7/6lZKSksJdTli8++672rNnj2pqamS32/Xqq6+Gu6QB5Xa7df/992vlypW65pprwl1O2AzPmPYNJSYmKioqSq2trb3aW1tblZKS0uc2y5cv14MPPuj7fwY33HCD7/lJ//AP/zBof6wDmYvLLrtMmzdv1ldffaX/+7//04QJE7RkyRJNmjRpIEo2SkpKSp9zFx8fz9EVaOPGjXrkkUe0adOmc04dDicTJ06UdPrfzdbWVj355JOaM2dOmKsaOCdOnNCePXu0d+9elZeXSzp9mtDr9WrEiBH67W9/q29/+9thrjL0BuevZJhFR0dr+vTpcjgcvjaPxyOHw6GCgoI+tzl16tQ5oSQqKkqS5B3Ej3MKZC7OiI2NVWpqqr7++mv9+te/1g9+8INQl2ucgoKCXnMnSdu2bbvg3GHoe/XVV1VWVqZXX31Vs2bNCnc5xvB4POrq6gp3GQMqPj5ef/jDH9Tc3Ox7zZ8/X9dee62am5uVn58f7hIHBEdYAmS1WlVaWqqcnBzl5eXJbrfL5XKprKxMkjR37lylpqbKZrNJku666y6tXbtW06ZNU35+vg4ePKjly5frrrvu8gWXwcrfufj973+vlpYWZWdnq6WlRU8++aQ8Ho+eeOKJcO5GUJw8eVIHDx70vf/888/V3NysSy+9VFdeeaUqKyvV0tKin//855Kk+fPna/369XriiSf00EMP6a233tKvfvUrvfnmm+HahaDxdy4kqbm52bftkSNH1NzcrOjoaE2dOnWgyw8qf+eitrZWpaWleu6555Sfny+n0ynp9OL0hISEsOxDMPg7D9XV1bryyis1ZcoUSacv9169erUeffTRsNQfTP7MRWRkpDIzM3ttn5SUpNjY2HPah7QwX6U0qK1bt8575ZVXeqOjo715eXne999/3/dZYWGht7S01Pe+p6fH++STT3qvvvpqb2xsrDctLc37t3/7t95jx44NfOEh4M9c7Nixw3vdddd5Y2JivOPHj/c++OCD3paWljBUHXxvv/22V9I5rzP7X1pa6i0sLDxnm+zsbG90dLR30qRJ3pdffnnA6w6FQOair/5XXXXVgNcebP7ORWFh4Xn7D1b+zsPzzz/vvf76672XXHKJNz4+3jtt2jTvT37yE6/b7Q7PDgRRIP99/LnheFlzhNc7iM9HAACAYYE1LAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAY7/8BWwJxaZgxi6oAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#  must jettison some inHD map-boundary units to connect the enclave to the larger complement\n",
    "#For each HD to be triaged, define the list(s) of contiguous enclave units that are on the map boundary, \n",
    "#  and the in-HD map-boundary segments.  ID which in-HD MBS's adjoin one vs. two enclaves.\n",
    "#     Fill all internal enclaves, and all boundary enclaves < 0.05 aDP.\n",
    "#   Then each loop, look for boundary enclaves that can be filled without overpopping.\n",
    "#     If none, pick the 1/2 has-1-boundary-enclave-neighbor that is least painful to jettison to free an enclave.\n",
    "#       (Jettison score based on pop-to-Free and distance from HDcP)\n",
    "#         Update HDpop, HDlist, and enclave & HD-boundary associations, then re-loop\n",
    "# Later, we will swell-fill to square up pop (w/ checkEnclave) biasing toward close-to-hdCP, underused\n",
    "borderSet = set(borderUnits)\n",
    "debug, debugPlot = False, False\n",
    "startTime = time.time()  #takes about 1.5sec per HD triage\n",
    "clusterJettisonBoost = 3.  #this discourages jettisoning of underused clusters\n",
    "print(\"this code will solve large enclaves so HD and complement are contiguous for\",len(cantFill),\"HDs\")\n",
    "nEnclavesPerHD = [0 for t in range(nHDs)]\n",
    "HDenclaveLists = [list() for t in range(nHDs)]\n",
    "for iii,t in enumerate(cantFill):\n",
    "    if iii %20 == 0:\n",
    "        print(\"working on HD\",t,\"cantFill\",iii,\"of\",len(cantFill),\".Time is now\",int(time.time() - startTime) )\n",
    "    shedUs = list()    \n",
    "    enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "    nEnclavesPerHD[t] = len(enclaveLists)\n",
    "    HDenclaveLists[t] = enclaveLists.copy()\n",
    "    if debug:\n",
    "        print(\"pre-adjust HDpop for HD\",t,\"is\",HDvPop[t],\"I found\",len(enclaveLists),\"TOTAL enclaves\")\n",
    "    internalEnclaves, edgeEnclaves, combinedEnclBorderList = list(), list(), list()\n",
    "    for jjj, eL in enumerate(enclaveLists.copy() ):\n",
    "        enclaveBorderList = list ( set(eL).intersection(borderSet) )\n",
    "        if debug:\n",
    "            print(\"In enclave\",jjj,\"I found\",len(enclaveBorderList),\"units that were enclaved and are in the map borderSet\")\n",
    "        combinedEnclBorderList += enclaveBorderList\n",
    "        ePop = np.sum( [unitPop[u] for u in eL] )\n",
    "        if len(enclaveBorderList) == 0 or ePop < 0.05 * aDP:  #internal or small enclave.  Fill it\n",
    "            if ePop > 0:  #in a prev treatment, could be an empty (surrounded) unit that we don't care about\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += ePop\n",
    "            internalEnclaves.append(eL)\n",
    "        else:\n",
    "            edgeEnclaves.append(eL)\n",
    "    if debug:\n",
    "        print(\"Of these\",len(internalEnclaves),\"were internal or small, so I filled them, leaving\",\n",
    "              len(edgeEnclaves),\"non-small border = edge enclaves\" )\n",
    "    if debugPlot:\n",
    "        print(\"Here is the original HD, internal enclaves ('x') and non-small border enclaves by enclave no\")\n",
    "        plotPoly(hdCP[t].buffer(0.3))\n",
    "        for u in HDunitList[t]:\n",
    "            if unitPop[u] > 0:\n",
    "                plotPoly(unitGeom[u],0.2)\n",
    "        for L in internalEnclaves:\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u],1.5)\n",
    "                    plotCenter(\"x\",unitCP[u],8)\n",
    "        for L in edgeEnclaves: #############\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    #plotCenter(\"e\",unitCP[u],8)\n",
    "        #plotPoly(MAP,0.4)\n",
    "        #plt.show()\n",
    "    enclaveLists, combinedEnclBorderSet = list(), set(combinedEnclBorderList)\n",
    "    if len(edgeEnclaves) > 0:\n",
    "        # Now, we order by chain connectivity of HD and enclave sublists to be H0-E0-H1-E1 ... En-Hn+1\n",
    "        nonHDborderSet = borderSet.difference(  set(HDunitList[t]) )\n",
    "        nonHDlongBorderSet = nonHDborderSet.difference(combinedEnclBorderSet) #long=non HD boundary excluding enclaves\n",
    "        remainingEnclBorderSet = combinedEnclBorderSet.copy()\n",
    "        remainingHDborderSet =   borderSet.intersection(set(HDunitList[t]) )\n",
    "        #Now find the \"HDender\" unit which borders the non-enclave nonHD boundary, then find opp end of this HD bdry piece\n",
    "        HDender = list(set(getAdjoiners(nonHDlongBorderSet,unitNbrs)).intersection(remainingHDborderSet) )[0]\n",
    "        firstHDborderSet = getContigFromStarter(HDender,remainingHDborderSet,unitNbrs)\n",
    "        HDstarter = list(set(getAdjoiners(remainingEnclBorderSet,unitNbrs)).intersection(firstHDborderSet))[0]\n",
    "        HDborderSublists = [ list(firstHDborderSet) ]\n",
    "        unused = [1 for eL in edgeEnclaves]\n",
    "        eLadjoiners = [getAdjoiners(eL,unitNbrs) for eL in edgeEnclaves ]\n",
    "        for j in range(len(edgeEnclaves)): #find the enclave with the most HDstarter neighbors (at least 1)\n",
    "            found = False\n",
    "            for i,eL in enumerate(edgeEnclaves):\n",
    "                if unused[i] == 1:\n",
    "                    HDadjSet = set(HDborderSublists[j]).intersection(set(eLadjoiners[i]))\n",
    "                    if len(HDadjSet) > 0:\n",
    "                        unused[i] = 0\n",
    "                        eNo = i\n",
    "                        found = True\n",
    "                        remainingEnclBorderSet = remainingEnclBorderSet.difference(set(edgeEnclaves[eNo]) )\n",
    "                        enclaveLists.append(edgeEnclaves[eNo])\n",
    "                        remainingHDborderSet = remainingHDborderSet.difference(set(HDborderSublists[j]) )\n",
    "                        HDstarter = list(set(eLadjoiners[i]).intersection(remainingHDborderSet))[0]       \n",
    "                        HDborderSublists.append(getContigFromStarter(HDstarter,remainingHDborderSet,unitNbrs) )\n",
    "                        break\n",
    "                if found:\n",
    "                    break\n",
    "\n",
    "        enclavePops = [np.sum([unitPop[u] for u in L]) for L in enclaveLists]\n",
    "        #print(\"prior to adjustments, border enclavePops are\",enclavePops)         \n",
    "\n",
    "        nHDBS, nEnclaves = len(HDborderSublists), len(enclaveLists)\n",
    "        popToFree, freeingCounties, freeingUnits = [0. for n in range(nHDBS)], [list() for n in range(nHDBS) \n",
    "                                                                               ],[list() for n in range(nHDBS)]\n",
    "        for j,L in enumerate(HDborderSublists):\n",
    "            for u in L:\n",
    "                if int(allUnits[u]) == allUnits[u]:  #this border unit is a vtd\n",
    "                    c = countyNo[parentVTDno[allUnits[u]]]   #countyNo[allUnits[u]]\n",
    "                    if c not in freeingCounties[j]:\n",
    "                        if countyPop[c] < 0.1 * aDP:  #plan to add all in-county pop\n",
    "                            freeingCounties[j].append(c)\n",
    "                        else:\n",
    "                            popToFree[j] += unitPop[u]\n",
    "                            freeingUnits[j].append(u)\n",
    "                else: #border unit is a full county or cluster, or in a dense county.  Add the whole unit pop\n",
    "                    popToFree[j] += unitPop[u]\n",
    "                    freeingUnits[j].append(u)\n",
    "            for c in freeingCounties[j]:  #include ALL in-HD pop from each non-unit border county, not just its border units\n",
    "                #plotPoly(countyGeom[c],0.1)\n",
    "                for u in countyUnitList[c]:\n",
    "                    if u in HDunitList[t]:\n",
    "                        popToFree[j] += unitPop[u]\n",
    "                        freeingUnits[j].append(u)\n",
    "        freeingCP = [ getHDcp(  unitCP, unitPop, L) for L in freeingUnits ]\n",
    "        freeingScore = [ pTF/aDP - 0.5*freeingCP[j].distance(hdCP[t]) / avgDist[t] for j,pTF in enumerate(popToFree) ]\n",
    "        for j, fU in enumerate(freeingUnits):  #in above 0.5 is a fudgy\n",
    "            if len(barredJettisonSet.intersection(set(fU)) ) > 0: #has a cluster we want to hold onto\n",
    "                freeingScore[j] += clusterJettisonBoost #  ..so increase its score (make it harder to drop)\n",
    "        if debugPlot:\n",
    "            print(\"plotting the freeing units with negative numbers for each freeing group\")\n",
    "            for j,L in enumerate(freeingUnits):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        #plotPoly(unitGeom[u],0.5)\n",
    "                        plotCenter(\"-\"+str(j),unitGeom[u],6)\n",
    "                        pass\n",
    "            print(\"plotting the enclaveLists, with + numbers for each enclave\")\n",
    "            for j,L in enumerate(enclaveLists):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        plotPoly(unitGeom[u])\n",
    "                        plotCenter(j,unitCP[u],8)\n",
    "                        pass\n",
    "    \n",
    "    while len(enclaveLists) > 0:\n",
    "        if HDvPop[t] + np.min(enclavePops) < 1.05*aDP or np.min(enclavePops) < 0.05 * aDP:  #fill the smallest enclave\n",
    "            eNo = enclavePops.index(np.min(enclavePops))\n",
    "            HDunitList[t] += enclaveLists[eNo]\n",
    "            HDvPop[t] += enclavePops[eNo]\n",
    "            #print(t,\"=t. Absorbing enclave\",eNo,\"with pop\",enclavePops[eNo],\"HD total pop now\",int(HDfreePop[t]) )\n",
    "            enclaveBUs = list(set(enclaveLists[eNo]).intersection(set(borderUnits)) )\n",
    "            enclaveBpop = np.sum([unitPop[u] for u in enclaveBUs])\n",
    "            sNo, dropped_sNo = eNo, eNo+1\n",
    "            freeingScore[sNo] += freeingScore[sNo+1]+enclaveBpop/aDP\n",
    "            freeingUnits[sNo] += freeingUnits[sNo+1]+enclaveBUs\n",
    "            popToFree[sNo]    +=    popToFree[sNo+1]+enclaveBpop\n",
    "        else: #jettison one of the two end HD border lists; pick the less painful path to freedom\n",
    "            distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "            starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "            eNo, dropped_sNo = 0,0\n",
    "            if starterU not in freeingUnits[-1]:  #we can't drop the home unit, even to save an underused corner\n",
    "                if freeingScore[-1] < freeingScore[0] or starterU in freeingUnits[0]:\n",
    "                    eNo, dropped_sNo = len(enclaveLists)-1,len(enclaveLists)\n",
    "            barredIncluded0 = barredJettisonSet.intersection(set(freeingUnits[0]))\n",
    "            barredIncluded_1 = barredJettisonSet.intersection(set(freeingUnits[-1]))\n",
    "            #if len(barredIncluded0.union(barredIncluded_1)) > 0:\n",
    "            #    print(\"We are considering dropping an end unit to free from t\",t,\". Chosen, beg, end, scores are\")\n",
    "            #    print(dropped_sNo,barredIncluded0, barredIncluded_1, freeingScore[0], freeingScore[-1])\n",
    "            HDunitList[t] = list( set(HDunitList[t]).difference(set(freeingUnits[dropped_sNo]) ) )\n",
    "            HDvPop[t] -= popToFree[dropped_sNo]\n",
    "            #print(t,\"= t. Jettisoning HDborder\",dropped_sNo,\"with popToFree\",popToFree[dropped_sNo] )\n",
    "        del enclaveLists[eNo]\n",
    "        del enclavePops[eNo]\n",
    "        if debug:\n",
    "            print(t,\"= t. Now we have\",len(enclaveLists),\"left trapped.  Picked eNo, freeScores were\", eNo,freeingScore)\n",
    "        del freeingScore[dropped_sNo]\n",
    "        del freeingUnits[dropped_sNo]\n",
    "        del popToFree   [dropped_sNo] \n",
    "\n",
    "    #plotPoly(MAP,0.4)\n",
    "    if debugPlot:\n",
    "        plt.show()    \n",
    "        \n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t]*nDistricts\n",
    "print(\"after the MustFree code run, we have the following for cluster usage\")\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66e91b98-63a9-47db-af63-039ac47b1835",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ca9ce3cb-c150-4f39-ae55-f781d39d7f57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "postMustFreeUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c32dd108-073b-48aa-99ce-fea50c8d023c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([HDvPop[t] for t in popHDlist] )\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f58fe2d0-3c62-412f-8984-ff5d67ea9b10",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After above work, re-classification of contiguity problems?\n",
      "working on HD 0\n",
      "working on HD 500\n",
      "working on HD 1000\n",
      "working on HD 1500\n",
      "working on HD 2000\n",
      "working on HD 2500\n",
      "working on HD 3000\n",
      "working on HD 3500\n",
      "working on HD 4000\n",
      "working on HD 4500\n",
      "out of 4604 total HDs, there were 4159 110 6 329 HDs that were clean, discontig only, enclave-only, both problems after triage\n"
     ]
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"After above work, re-classification of contiguity problems?\")\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1ef683a2-4429-4a91-b1ea-a9b0950dea22",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on current HD diam for HD number 0\n",
      "working on current HD diam for HD number 1000\n",
      "working on current HD diam for HD number 2000\n",
      "working on current HD diam for HD number 3000\n",
      "working on current HD diam for HD number 4000\n",
      "here is the histogram of HD diameter = sqrt(area)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDdiam = [0. for t in range(nHDs)]\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%1000 == 0:\n",
    "        print(\"working on current HD diam for HD number\",t)\n",
    "    HDdiam[t] = (HDpoly[t].intersection(MAP) ).area ** 0.5\n",
    "plt.hist([HDdiam[t] for t in popHDlist])\n",
    "print(\"here is the histogram of HD diameter = sqrt(area)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "45e3c17c-8c16-4bc5-b7fc-25b9250ed11a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we will regrow all disco'd HDs off by more than 38469 but less than 192341\n",
      "... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\n",
      "This is a total of 439 HDs to triage in this block\n",
      "working on swell-filling discontig HD 99 our 1 th HD we think we can swell out of 439 0.0 sec elapsed\n",
      "working on swell-filling discontig HD 382 our 11 th HD we think we can swell out of 439 191.845 sec elapsed\n",
      "working on swell-filling discontig HD 639 our 21 th HD we think we can swell out of 439 192.128 sec elapsed\n",
      "working on swell-filling discontig HD 792 our 31 th HD we think we can swell out of 439 223.05 sec elapsed\n",
      "working on swell-filling discontig HD 964 our 41 th HD we think we can swell out of 439 328.299 sec elapsed\n",
      "working on swell-filling discontig HD 1253 our 51 th HD we think we can swell out of 439 352.286 sec elapsed\n",
      "working on swell-filling discontig HD 1615 our 61 th HD we think we can swell out of 439 462.932 sec elapsed\n",
      "working on swell-filling discontig HD 2591 our 71 th HD we think we can swell out of 439 472.332 sec elapsed\n",
      "working on swell-filling discontig HD 3168 our 81 th HD we think we can swell out of 439 637.806 sec elapsed\n",
      "working on swell-filling discontig HD 3751 our 91 th HD we think we can swell out of 439 841.448 sec elapsed\n",
      "working on swell-filling discontig HD 4396 our 101 th HD we think we can swell out of 439 933.854 sec elapsed\n",
      "working on swell-filling discontig HD 142 our 111 th HD we think we can swell out of 439 951.596 sec elapsed\n",
      "working on swell-filling discontig HD 909 our 121 th HD we think we can swell out of 439 1013.7 sec elapsed\n",
      "working on swell-filling discontig HD 1240 our 131 th HD we think we can swell out of 439 1065.001 sec elapsed\n",
      "working on swell-filling discontig HD 1265 our 141 th HD we think we can swell out of 439 1312.069 sec elapsed\n",
      "working on swell-filling discontig HD 1286 our 151 th HD we think we can swell out of 439 1764.328 sec elapsed\n",
      "working on swell-filling discontig HD 1304 our 161 th HD we think we can swell out of 439 1955.876 sec elapsed\n",
      "working on swell-filling discontig HD 1318 our 171 th HD we think we can swell out of 439 2236.264 sec elapsed\n",
      "working on swell-filling discontig HD 1355 our 181 th HD we think we can swell out of 439 2398.47 sec elapsed\n",
      "working on swell-filling discontig HD 1369 our 191 th HD we think we can swell out of 439 2551.642 sec elapsed\n",
      "working on swell-filling discontig HD 1383 our 201 th HD we think we can swell out of 439 2749.123 sec elapsed\n",
      "working on swell-filling discontig HD 1400 our 211 th HD we think we can swell out of 439 2959.408 sec elapsed\n",
      "working on swell-filling discontig HD 1413 our 221 th HD we think we can swell out of 439 3624.435 sec elapsed\n",
      "working on swell-filling discontig HD 1441 our 231 th HD we think we can swell out of 439 3878.899 sec elapsed\n",
      "working on swell-filling discontig HD 1546 our 241 th HD we think we can swell out of 439 4124.662 sec elapsed\n",
      "working on swell-filling discontig HD 2124 our 251 th HD we think we can swell out of 439 4194.881 sec elapsed\n",
      "working on swell-filling discontig HD 2315 our 261 th HD we think we can swell out of 439 4276.108 sec elapsed\n",
      "working on swell-filling discontig HD 2687 our 271 th HD we think we can swell out of 439 4437.651 sec elapsed\n",
      "working on swell-filling discontig HD 2733 our 281 th HD we think we can swell out of 439 4875.287 sec elapsed\n",
      "working on swell-filling discontig HD 2877 our 291 th HD we think we can swell out of 439 5313.453 sec elapsed\n",
      "working on swell-filling discontig HD 2957 our 301 th HD we think we can swell out of 439 5562.301 sec elapsed\n",
      "working on swell-filling discontig HD 3104 our 311 th HD we think we can swell out of 439 5870.238 sec elapsed\n",
      "working on swell-filling discontig HD 3182 our 321 th HD we think we can swell out of 439 6050.623 sec elapsed\n",
      "working on swell-filling discontig HD 3259 our 331 th HD we think we can swell out of 439 6316.705 sec elapsed\n",
      "working on swell-filling discontig HD 3339 our 341 th HD we think we can swell out of 439 6489.342 sec elapsed\n",
      "working on swell-filling discontig HD 3421 our 351 th HD we think we can swell out of 439 6801.953 sec elapsed\n",
      "working on swell-filling discontig HD 3557 our 361 th HD we think we can swell out of 439 7364.396 sec elapsed\n",
      "working on swell-filling discontig HD 3657 our 371 th HD we think we can swell out of 439 7560.651 sec elapsed\n",
      "working on swell-filling discontig HD 3795 our 381 th HD we think we can swell out of 439 7737.722 sec elapsed\n",
      "working on swell-filling discontig HD 3916 our 391 th HD we think we can swell out of 439 7845.011 sec elapsed\n",
      "working on swell-filling discontig HD 4085 our 401 th HD we think we can swell out of 439 8231.029 sec elapsed\n",
      "working on swell-filling discontig HD 4114 our 411 th HD we think we can swell out of 439 8523.303 sec elapsed\n",
      "working on swell-filling discontig HD 4202 our 421 th HD we think we can swell out of 439 8878.105 sec elapsed\n",
      "working on swell-filling discontig HD 4260 our 431 th HD we think we can swell out of 439 9265.379 sec elapsed\n",
      "we partially regrew 366 HDs, leaving 79 to regrow from scratch\n"
     ]
    }
   ],
   "source": [
    "### THIS IS THE CANSWELL CODE  #Note -- this has not yet implemented the \"avoidedEnclave\" shortcut\n",
    "print(\"Here, we will regrow all disco'd HDs off by more than\",int(aDP-minPostFixPop),\"but less than\",int(0.25*aDP) )\n",
    "print(\"... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\")\n",
    "HDnAddedUnits = [0 for t in range(nHDs)]\n",
    "maxGap = 0.9 * np.median(unitPop)  #set a reasonable gap prior to picking the last block\n",
    "HDdiam = [HDpoly[t].area ** 0.5 for t in range(nHDs) ] #in case not defined before\n",
    "badDiscoList = list()\n",
    "nCanSwell = 0\n",
    "triageList = discontigOnly + bothProblems\n",
    "print(\"This is a total of\",len(triageList),\"HDs to triage in this block\")\n",
    "startTime = time.time()\n",
    " \n",
    "for i,t in enumerate(triageList): #canSwell\n",
    "    if i%10 == 0:\n",
    "        SEC = r3(time.time()-startTime)\n",
    "        print(\"working on swell-filling discontig HD\",t,\"our\",i+1,\"th HD we think we can swell out of\",len(triageList),SEC,\"sec elapsed\" )\n",
    "    distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "    starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = getContigFromStarter(starterU, HDunitList[t], unitNbrs)\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    if contigPop < 0.50 * aDP or contigPop > 2.*aDP - minPostFixPop:  #formerly < 0.75 aDP\n",
    "        badDiscoList.append(t)\n",
    "    else: #rebuild from this big piece\n",
    "        nCanSwell +=1\n",
    "        gap = aDP - contigPop\n",
    "        origGap = gap\n",
    "        currList, addedList = contigUs.copy(), list()\n",
    "        adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "        nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "        nearHDscore = [ (unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] for uu in adjoiners ] \n",
    "        stillGoing = True\n",
    "\n",
    "        while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "            while i < len(nearHDscore) and notYetPicked:        \n",
    "                listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "                canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "                if canAdd:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't add any more units without creating an enclave\n",
    "            else: #we selected the best unit to add legally\n",
    "                gap -= unitPop[unitNoToAdd]\n",
    "                addedList.append(unitNoToAdd)  #so add it to our growing HD ...\n",
    "                currList.append( unitNoToAdd)\n",
    "                for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                    if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append((unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "                            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] )\n",
    "                del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                for i, uu in enumerate(nearHDlist.copy()):\n",
    "                    if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                        del nearHDscore[nearHDlist.index(uu)]\n",
    "                        del nearHDlist[ nearHDlist.index(uu)]\n",
    "        for u in addedList:\n",
    "            unitUse[u] += HDweight[t] * nDistricts\n",
    "        for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "            unitUse[u] -= HDweight[t] * nDistricts       \n",
    "        HDunitList[t] = contigUs + addedList\n",
    "        HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "        HDnAddedUnits[t] = len(addedList)\n",
    "    \n",
    "badDiscoList += enclaveOnly\n",
    "print(\"we partially regrew\",nCanSwell,\"HDs, leaving\",len(badDiscoList),\"to regrow from scratch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f57325dd-523c-466a-80cb-8e10b739b4ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous avg use and its sd are 1.0154 0.1256\n",
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.01153 0.11685\n",
      "CCB cluster 0 = unit 4019 with pop 161018.0 now has use of 0.9812908809596604\n",
      "CCB cluster 1 = unit 4020 with pop 185562.0 now has use of 1.0196069383921682\n",
      "CCB cluster 2 = unit 4021 with pop 139686.0 now has use of 0.9109418768388854\n",
      "CCB cluster 3 = unit 4022 with pop 25126.0 now has use of 1.0095427831392623\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"previous avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0ce918e1-0d30-4930-9fb8-0769d793683c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 1000 time is now 6\n",
      "working on HD 2000 time is now 17\n",
      "working on HD 3000 time is now 26\n",
      "working on HD 4000 time is now 43\n",
      "out of 4604 total HDs, there were 4531.0 contiguous and 4506.0 complement-contiguous HDs\n",
      "41 HDs had both discontiguity problems, while enclave-only = 57 and discontig only= 32\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 73 57\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%1000 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "463db75a-694e-4c7d-9693-e10f3b4d65db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 730895 district pop vs 769364 target\n",
      "we'll also trim any HD with total islands' pop <= 15387\n",
      "working on HD 142\n",
      "Out of 73 discontig HDs 73 had discontig HDs.\n",
      "Of these, 11 won't be under 730895 after all minor discontigys were shed, while 62 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "There are 62 HDs still with both discontinuity problems\n",
      "Additionally, 0 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%100 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for t in sPgenerator:    \n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "39d37c15-759f-462c-b503-047b6b5e1554",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "110 329 62\n",
      "{4096, 3076, 4101, 4102, 4103, 3078, 3079, 4109, 4110, 4112, 4113, 4114, 4115, 4121, 4123, 3103, 3104, 3105, 4131, 4132, 1061, 4133, 4134, 3107, 3110, 3113, 1073, 1077, 1078, 2103, 4165, 1107, 3156, 2133, 4186, 3163, 3165, 2143, 3168, 4192, 99, 3171, 106, 4202, 3180, 3182, 4212, 2166, 4218, 4219, 1148, 1150, 3203, 3204, 1157, 4229, 3205, 3209, 4250, 4254, 4255, 4256, 4257, 4260, 4261, 3240, 3241, 3248, 2232, 3257, 3258, 3259, 3260, 4285, 2238, 3261, 3262, 3266, 4305, 1234, 3282, 1237, 1239, 2264, 2265, 1240, 3289, 1242, 1243, 1244, 1249, 2273, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1260, 1263, 1265, 1267, 1268, 1269, 3317, 3319, 4344, 1274, 2298, 252, 253, 1275, 1277, 256, 1282, 1283, 1284, 1285, 3333, 1286, 1287, 4361, 1288, 2315, 1289, 3339, 3340, 1296, 1298, 1299, 1300, 1303, 1304, 1305, 1306, 4379, 3354, 3355, 1309, 1311, 4383, 4384, 1310, 3361, 1313, 1314, 3366, 1315, 1316, 1317, 1318, 4396, 1320, 1321, 1323, 307, 1331, 1334, 4408, 1336, 1337, 3386, 1342, 2370, 1348, 3397, 2374, 4422, 1350, 1352, 1355, 1356, 1357, 1358, 1360, 1362, 1363, 3411, 1365, 1366, 1367, 1369, 1371, 1372, 3420, 3421, 4447, 3423, 1375, 1376, 1377, 1378, 1379, 4454, 1381, 1382, 361, 1383, 363, 2412, 4459, 1384, 1385, 1387, 1389, 1394, 4467, 1395, 1396, 1398, 1399, 1400, 1401, 4474, 4475, 3447, 1404, 382, 383, 1405, 1406, 1407, 3454, 4485, 1410, 1411, 4488, 1412, 1413, 1416, 1417, 1419, 1425, 1426, 1427, 1428, 1437, 1438, 1441, 1442, 1444, 1446, 2471, 3508, 3510, 3512, 3515, 3516, 3521, 2511, 3541, 2522, 2523, 3549, 3557, 2536, 3561, 3570, 3571, 1524, 3580, 3582, 3588, 1548, 3597, 3610, 3364, 3630, 3637, 3638, 2626, 3657, 588, 3661, 3662, 3660, 3663, 3667, 3670, 3671, 2652, 3678, 628, 629, 630, 631, 638, 639, 640, 641, 2686, 2687, 2688, 2691, 647, 649, 651, 2699, 2701, 654, 655, 3726, 3723, 2722, 2724, 2725, 3751, 2729, 2731, 2733, 3764, 3773, 2763, 2768, 2769, 2771, 2772, 3795, 2780, 3814, 2792, 746, 3819, 753, 3828, 764, 767, 2835, 3868, 3869, 3870, 3871, 1833, 2865, 2866, 2889, 3916, 3918, 2896, 2901, 859, 2907, 862, 887, 3962, 3965, 2947, 2949, 2951, 2954, 2955, 2956, 909, 1934, 1936, 1937, 2963, 1940, 3987, 3988, 3989, 3991, 3994, 2972, 936, 941, 4016, 2996, 962, 3015, 4046, 3042, 3059, 4085, 4086, 4095}\n"
     ]
    }
   ],
   "source": [
    "print(len(discontigOnly),len(bothProblems), len(badDiscoList))\n",
    "print((set(discontigOnly).union(set(bothProblems)) ) .difference(set(badDiscoList)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "66b4f706-b505-4a89-868c-5a01f86c7c75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "print(iii)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "2e54bac4-c327-473d-bbb4-257486bddd20",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\n",
      "Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\n",
      "This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is 62\n",
      "swell-generating HD 1615 our 1 th HD we must regrow out of 62 0 sec elapsed\n",
      "swell-generating HD 2597 our 11 th HD we must regrow out of 62 2531 sec elapsed\n",
      "swell-generating HD 4294 our 21 th HD we must regrow out of 62 3104 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "#THIS IS THE MUSTSPAWN code block\n",
    "print(\"And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\")\n",
    "print(\"Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\")\n",
    "print(\"This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is\",len(badDiscoList))\n",
    "avgDiam = MAP.area**0.5 / float(nDistricts)\n",
    "startTime = time.time()\n",
    "for iii,t in enumerate(badDiscoList[38:]):  #MO -- restarted\n",
    "    if iii%10 == 0:\n",
    "        print(\"swell-generating HD\",t,\"our\",iii+1,\"th HD we must regrow out of\",len(badDiscoList),int(time.time()-startTime),\"sec elapsed\" )\n",
    "    notInCluster = True\n",
    "    for jj, geo in enumerate(CCBgeom):  #swell in-corner HDs from the corner cluster\n",
    "        if geo.contains(hdCP[t]):\n",
    "            starterU = allUnits.index(jj+0.25)\n",
    "            notInCluster = False\n",
    "    if notInCluster:\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDunitList[t], unitNbrs) in above code block\n",
    "    altContigUs = getContigFromStarter(starterU, HDunitList[t], unitNbrs)\n",
    "    altContigPop = np.sum( [ unitPop[u] for u in altContigUs ] )\n",
    "    if abs( altContigPop/aDP - 1.0) < 0.4 : #NOTE FOR MO -- MAKING THIS MORE LENIENT; only regrow from scratch if contig pop > 40% off\n",
    "        contigUs = altContigUs.copy()\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    nearHDscore = [ (0.2*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] for uu in adjoiners ]\n",
    "    stillGoing = True\n",
    "    \n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((0.1*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "                                                                        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28f2fa80-724b-4475-a083-e1441700a096",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NOTE -- IN THE ABOVE, I stopped after 38 drawn, then re-started, fearing a Chrome reboot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2290067b-f260-4511-8bdd-dd6481100ff4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38\n"
     ]
    }
   ],
   "source": [
    "print(iii)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3842f896-d599-4549-982a-7810a2caaabb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MO - only -- intermediate save in case Chrome reboots\n"
     ]
    }
   ],
   "source": [
    "print(\"MO - only -- intermediate save in case Chrome reboots\")\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"opt3mostlyContigButOffPop.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ff007e9b-fb7d-45f2-90be-c1aec50fbe31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will now ensure all CCB's are used at least 1.02 prior to ensuring all HDs have pop near 769364.125\n",
      "we'll do this by adding to neighboring HDs, biasing toward close, underpopped HDs\n",
      "now working to add CCB no 0 = unit 4019 to more HDs.  Its current usage is 0.9536303762539086\n",
      "examining 0 th best-scoring HD that is adjacent to CCB 0\n",
      "examining 10 th best-scoring HD that is adjacent to CCB 0\n",
      "13 HDs examined to boost CCBno 0 with pop 161018.0 ; it's now used 0.98054\n",
      "0 HDs examined to boost CCBno 1 with pop 185562.0 ; it's now used 1.04263\n",
      "now working to add CCB no 2 = unit 4021 to more HDs.  Its current usage is 0.884258802683328\n",
      "examining 0 th best-scoring HD that is adjacent to CCB 2\n",
      "examining 10 th best-scoring HD that is adjacent to CCB 2\n",
      "examining 20 th best-scoring HD that is adjacent to CCB 2\n",
      "examining 30 th best-scoring HD that is adjacent to CCB 2\n",
      "examining 40 th best-scoring HD that is adjacent to CCB 2\n",
      "examining 50 th best-scoring HD that is adjacent to CCB 2\n",
      "examining 60 th best-scoring HD that is adjacent to CCB 2\n",
      "examining 70 th best-scoring HD that is adjacent to CCB 2\n",
      "examining 80 th best-scoring HD that is adjacent to CCB 2\n",
      "examining 90 th best-scoring HD that is adjacent to CCB 2\n",
      "examining 100 th best-scoring HD that is adjacent to CCB 2\n",
      "examining 110 th best-scoring HD that is adjacent to CCB 2\n",
      "113 HDs examined to boost CCBno 2 with pop 139686.0 ; it's now used 0.98048\n",
      "now working to add CCB no 3 = unit 4022 to more HDs.  Its current usage is 0.9781545246862184\n",
      "examining 0 th best-scoring HD that is adjacent to CCB 3\n",
      "2 HDs examined to boost CCBno 3 with pop 25126.0 ; it's now used 0.98119\n"
     ]
    }
   ],
   "source": [
    "#new for MO - let's shore up underused CCB's before we pop-patch\n",
    "minCCBuse_ = 0.98\n",
    "print(\"we will now ensure all CCB's are used at least\",minCCBuse,\"prior to ensuring all HDs have pop near\",aDP)\n",
    "print(\"we'll do this by adding to neighboring HDs, biasing toward close, underpopped HDs\")\n",
    "\n",
    "for j in range(nCCBs):\n",
    "    idxNo = 0\n",
    "    jNo = allUnits.index(j+0.25)\n",
    "    if unitUse[jNo] < minCCBuse:\n",
    "        print(\"now working to add CCB no\",j,\"= unit\",jNo,\"to more HDs.  Its current usage is\",unitUse[jNo])\n",
    "        addCandidates, addDists, addPopRatios, addScores = list(), list(), list(), list()\n",
    "        for t in popHDlist:\n",
    "            if jNo not in HDunitList[t] and len( set(unitNbrs[jNo]).intersection(set(HDunitList[t])) ) > 0:\n",
    "                addCandidates.append(t)\n",
    "                addDists.append( hdCP[t].distance(unitCP[jNo]) )\n",
    "                addPopRatios.append( (HDvPop[t] + unitPop[jNo] )/aDP )\n",
    "                i = addCandidates.index(t)\n",
    "                addScores.append( addPopRatios[i] - 1.0 + addDists[i]/HDdiam[t] )  #bias toward nonoverpopped, close HDs\n",
    "        idx, idxNo = np.argsort(addScores), 0 \n",
    "    \n",
    "        while idxNo < len(addCandidates) and unitUse[jNo] < minCCBuse_ :\n",
    "            if idxNo %10 == 0:\n",
    "                print(\"examining\",idxNo,\"th best-scoring HD that is adjacent to CCB\",j)   \n",
    "            t = addCandidates[idx[idxNo]]\n",
    "            cContig = avoidedEnclave( HDunitList[t], unitNbrs,[jNo] )\n",
    "            if cContig:\n",
    "                HDunitList[t].append(jNo)\n",
    "                HDvPop[t] += unitPop[jNo]\n",
    "                unitUse[jNo] += HDweight[t] * nDistricts\n",
    "            idxNo +=1 \n",
    "    print(idxNo,\"HDs examined to boost CCBno\",j,\"with pop\",unitPop[jNo],\"; it's now used\",r5(unitUse[jNo]) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5be77fe4-948d-41a5-80ae-14da9e58018d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "42ddb4c6-a84a-45ec-be3e-bb9d1af51e20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(not-so-)final check -- any more disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 500 time is now 3 sec\n",
      "working on HD 1000 time is now 12 sec\n",
      "working on HD 1500 time is now 32 sec\n",
      "working on HD 2000 time is now 37 sec\n",
      "working on HD 2500 time is now 42 sec\n",
      "working on HD 3000 time is now 50 sec\n",
      "working on HD 3500 time is now 67 sec\n",
      "working on HD 4000 time is now 81 sec\n",
      "working on HD 4500 time is now 90 sec\n",
      "out of 4604 total HDs, there were 4545 0 59 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 90 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"(not-so-)final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "220b2fdf-a2a6-4b17-875d-20b6fd460b8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 807832 district pop vs 769364 target\n",
      "working on enclave-y HD 767 . We have evaluated 0 of 59 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 3209 . We have evaluated 40 of 59 enclavy HDs.Time is now 4\n",
      "Of these, 59 wouldn't be over 807832 if all enclaves filled, while 0 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#if still enclaves -- rerun the CANFILL CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(enclaveOnly):  #internals + edgers):\n",
    "    if iii%40 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(enclaveOnly),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b78199da-ac7c-4cfb-ac0b-26363a8a8c56",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prev avg use and its sd are 1.01153 0.11685\n",
      "defining and displaying current unit use after most recent manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.01395 0.11265\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"prev avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after most recent manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a2242181-3a07-40b7-ae56-bbe47bd2dc79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(not-so-)final check -- any more disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 500 time is now 2 sec\n",
      "working on HD 1000 time is now 5 sec\n",
      "working on HD 1500 time is now 9 sec\n",
      "working on HD 2000 time is now 12 sec\n",
      "working on HD 2500 time is now 15 sec\n",
      "working on HD 3000 time is now 19 sec\n",
      "working on HD 3500 time is now 26 sec\n",
      "working on HD 4000 time is now 33 sec\n",
      "working on HD 4500 time is now 39 sec\n",
      "out of 4604 total HDs, there were 4604 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 39 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"(not-so-)final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "de4d8af8-cc64-4215-9b95-f4c6a2bd03af",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedAgainUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f4bb76c7-0ae0-40cb-9c6a-ee436dfc30c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDunitList = [savedAgainUnitList[t].copy() for t in range(nHDs)]  #restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f3bcd6a8-40b6-4764-a0d1-6814e3286cb8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking on our corner clusters and unit county usage\n",
      "usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\n",
      "1.08193     0     11637 0.5\n",
      "0.92143     1     10567 0.5\n",
      "1.09635     2     8815 0.5\n",
      "1.36911     3     8495 0.5\n",
      "0.99696     4     5202 0.5\n",
      "1.35095     5     14188 0.5\n",
      "1.12971     6     7408 0.5\n",
      "1.21118     7     7569 0.5\n",
      "1.01769     8     8430 0.5\n",
      "1.17884     9     14421 0.5\n",
      "0.9896     10     11578 0.5\n",
      "1.26787     11     14794 0.5\n",
      "0.99587     12     9808 0.5\n",
      "1.25566     13     8279 0.5\n",
      "1.01793     14     10151 0.5\n",
      "1.09694     15     9537 0.5\n",
      "1.09435     16     11874 0.5\n",
      "1.1189     17     14557 0.5\n",
      "0.941     18     15209 0.5\n",
      "1.0185     19     12626 0.5\n",
      "1.12193     20     8432 0.5\n",
      "0.91503     21     3538 0.5\n",
      "0.94574     22     8666 0.5\n",
      "1.12931     23     11322 0.5\n",
      "1.03126     24     8635 0.5\n",
      "1.19986     25     13274 0.5\n",
      "0.98436     26     8553 0.5\n",
      "0.89729     27     4681 0.5\n",
      "1.09157     28     10355 0.5\n",
      "1.07858     29     6096 0.5\n",
      "0.9599     30     10679 0.5\n",
      "1.41732     31     9284 0.5\n",
      "0.8176     32     4032 0.5\n",
      "1.15635     33     7031 0.5\n",
      "0.86308     34     6103 0.5\n",
      "0.94117     35     5999 0.5\n",
      "0.99427     36     10974 0.5\n",
      "0.98054     4019     161018 0.25\n",
      "1.04263     4020     185562 0.25\n",
      "0.98072     4021     139686 0.25\n",
      "0.98119     4022     25126 0.25\n"
     ]
    }
   ],
   "source": [
    "print(\"checking on our corner clusters and unit county usage\")\n",
    "print(\"usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\")\n",
    "for u in range(nUnits):\n",
    "    if int(allUnits[u]) != allUnits[u] :\n",
    "        print(r5(unitUse[u]),\"   \",u,\"   \",int(unitPop[u]), allUnits[u]%1 )\n",
    "# note for MN, option 3 here led to tight CCB unit usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "1e983c03-777a-4e81-bed0-34a620175f83",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's visualize over-used and underused units, < 0.75 or > 1.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here is the histogram of HD pops relative to aDP\n"
     ]
    },
    {
     "data": {
      "image/png": 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27ZWWlqYePXro448/lsPh8O4jJydHHTt2VL9+/TRw4EDdfvvtPp/x4nQ6tXbtWh06dEg9evTQs88+q0mTJvEINXARZyqq9FROwcUHAoDhbJZlWYEuwh88Ho+cTqdKS0u5HwZXjdPllYqf9OF5+7k/BkBDd6nv33wXEgAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwQBBp0ihUe6elBLoMAPC7ev8yRwBXVuuJqwJdAgBccZyBAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA49Q6wOTl5WnQoEGKjY2VzWbTihUrvH0VFRWaMGGCunbtqqZNmyo2NlYjRozQkSNHfPbRunVr2Ww2n2XGjBk+Y3bu3KnevXurcePGiouL08yZM+s2QwAAEHRqHWBOnTqlhIQEzZkz55y+06dPa/v27frTn/6k7du3a9myZSoqKtK99957zthp06bp6NGj3uXpp5/29nk8HiUnJ6tVq1YqKCjQrFmzNGXKFL399tu1LRcAAAShsNpuMGDAAA0YMKDGPqfTqdzcXJ+2//zP/1TPnj1VXFysG264wdseEREhl8tV435ycnJUXl6u+fPny263q3PnziosLNTs2bM1ZsyY2pYMAACCjN/vgSktLZXNZlOzZs182mfMmKEWLVropptu0qxZs1RZWenty8/PV58+fWS3271tKSkpKioq0o8//ljjccrKyuTxeHwWAAAQnGp9BqY2zpw5owkTJujhhx9WZGSkt/2ZZ57RzTffrObNm2vLli3KysrS0aNHNXv2bEmS2+1WmzZtfPYVHR3t7bvmmmvOOVZ2dramTp3qx9kAAICGwm8BpqKiQg888IAsy9LcuXN9+jIzM70/d+vWTXa7XY8//riys7PlcDjqdLysrCyf/Xo8HsXFxdWteAAA0KD5JcCcDS9fffWVNmzY4HP2pSaJiYmqrKzU4cOH1aFDB7lcLpWUlPiMObt+vvtmHA5HncMPAAAwS73fA3M2vHz55Zdat26dWrRocdFtCgsLFRISoqioKElSUlKS8vLyVFFR4R2Tm5urDh061Hj5CAAAXF1qfQbm5MmT2r9/v3f90KFDKiwsVPPmzRUTE6P7779f27dv18qVK1VVVSW32y1Jat68uex2u/Lz8/Xpp5+qb9++ioiIUH5+vsaNG6dHHnnEG06GDh2qqVOnKi0tTRMmTNDu3bv12muv6ZVXXqmnaQMAAJPZLMuyarPBxo0b1bdv33PaR44cqSlTppxz8+1ZH330ke68805t375dTz31lL744guVlZWpTZs2Gj58uDIzM30uAe3cuVPp6en67LPP1LJlSz399NOaMGHCJdfp8XjkdDpVWlp60UtYgMlaT1x1yWMPz0j1YyUAcPku9f271gHGFAQYXC0IMACCyaW+f/NdSAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOLUOMHl5eRo0aJBiY2Nls9m0YsUKn37LsjRp0iTFxMSoSZMm6t+/v7788kufMT/88IOGDRumyMhINWvWTGlpaTp58qTPmJ07d6p3795q3Lix4uLiNHPmzNrPDgAABKVaB5hTp04pISFBc+bMqbF/5syZev311/XWW2/p008/VdOmTZWSkqIzZ854xwwbNkx79uxRbm6uVq5cqby8PI0ZM8bb7/F4lJycrFatWqmgoECzZs3SlClT9Pbbb9dhigAAINjYLMuy6ryxzably5dr8ODBkn4++xIbG6tnn31Wzz33nCSptLRU0dHRWrhwoR566CHt27dP8fHx+uyzz3TLLbdIktasWaOBAwfqm2++UWxsrObOnavnn39ebrdbdrtdkjRx4kStWLFCX3zxxSXV5vF45HQ6VVpaqsjIyLpOEWjwWk9cdcljD89I9WMlAHD5LvX9u17vgTl06JDcbrf69+/vbXM6nUpMTFR+fr4kKT8/X82aNfOGF0nq37+/QkJC9Omnn3rH9OnTxxteJCklJUVFRUX68ccfazx2WVmZPB6PzwIAAIJTvQYYt9stSYqOjvZpj46O9va53W5FRUX59IeFhal58+Y+Y2raxy+P8c+ys7PldDq9S1xc3OVPCAAANEhB8xRSVlaWSktLvcvXX38d6JIAAICf1GuAcblckqSSkhKf9pKSEm+fy+XSsWPHfPorKyv1ww8/+IypaR+/PMY/czgcioyM9FkAAEBwqtcA06ZNG7lcLq1fv97b5vF49OmnnyopKUmSlJSUpOPHj6ugoMA7ZsOGDaqurlZiYqJ3TF5enioqKrxjcnNz1aFDB11zzTX1WTIAADBQrQPMyZMnVVhYqMLCQkk/37hbWFio4uJi2Ww2jR07Vi+99JI++OAD7dq1SyNGjFBsbKz3SaVOnTrpnnvu0ejRo7Vt2zZt3rxZGRkZeuihhxQbGytJGjp0qOx2u9LS0rRnzx4tWbJEr732mjIzM+tt4gAAwFxhtd3g888/V9++fb3rZ0PFyJEjtXDhQv3hD3/QqVOnNGbMGB0/fly333671qxZo8aNG3u3ycnJUUZGhvr166eQkBANGTJEr7/+urff6XRq7dq1Sk9PV48ePdSyZUtNmjTJ57NiAADA1euyPgemIeNzYHC14HNgAASTgHwODAAAwJVAgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxqn3ANO6dWvZbLZzlvT0dEnSnXfeeU7fE0884bOP4uJipaamKjw8XFFRURo/frwqKyvru1QAAGCosPre4Weffaaqqirv+u7du3X33XfrX/7lX7xto0eP1rRp07zr4eHh3p+rqqqUmpoql8ulLVu26OjRoxoxYoQaNWqk6dOn13e5AADAQPUeYK699lqf9RkzZqhdu3a64447vG3h4eFyuVw1br927Vrt3btX69atU3R0tLp3764XX3xREyZM0JQpU2S32+u7ZAAAYBi/3gNTXl6u//7v/9Zjjz0mm83mbc/JyVHLli3VpUsXZWVl6fTp096+/Px8de3aVdHR0d62lJQUeTwe7dmz57zHKisrk8fj8VkAAEBwqvczML+0YsUKHT9+XL///e+9bUOHDlWrVq0UGxurnTt3asKECSoqKtKyZcskSW632ye8SPKuu93u8x4rOztbU6dOrf9JAACABsevAWbevHkaMGCAYmNjvW1jxozx/ty1a1fFxMSoX79+OnDggNq1a1fnY2VlZSkzM9O77vF4FBcXV+f9AQCAhstvAearr77SunXrvGdWzicxMVGStH//frVr104ul0vbtm3zGVNSUiJJ571vRpIcDoccDsdlVg0AAEzgt3tgFixYoKioKKWmpl5wXGFhoSQpJiZGkpSUlKRdu3bp2LFj3jG5ubmKjIxUfHy8v8oFAAAG8csZmOrqai1YsEAjR45UWNj/HuLAgQNatGiRBg4cqBYtWmjnzp0aN26c+vTpo27dukmSkpOTFR8fr+HDh2vmzJlyu9164YUXlJ6ezhkWAAAgyU8BZt26dSouLtZjjz3m026327Vu3Tq9+uqrOnXqlOLi4jRkyBC98MIL3jGhoaFauXKlnnzySSUlJalp06YaOXKkz+fGAACAq5tfAkxycrIsyzqnPS4uTps2bbro9q1atdLq1av9URoAAAgCfBcSAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADBOvQeYKVOmyGaz+SwdO3b09p85c0bp6elq0aKFfvWrX2nIkCEqKSnx2UdxcbFSU1MVHh6uqKgojR8/XpWVlfVdKgAAMFSYP3bauXNnrVu37n8PEva/hxk3bpxWrVqlpUuXyul0KiMjQ/fdd582b94sSaqqqlJqaqpcLpe2bNmio0ePasSIEWrUqJGmT5/uj3IBAIBh/BJgwsLC5HK5zmkvLS3VvHnztGjRIt11112SpAULFqhTp07aunWrbrvtNq1du1Z79+7VunXrFB0dre7du+vFF1/UhAkTNGXKFNntdn+UDAAADOKXe2C+/PJLxcbGqm3btho2bJiKi4slSQUFBaqoqFD//v29Yzt27KgbbrhB+fn5kqT8/Hx17dpV0dHR3jEpKSnyeDzas2fPeY9ZVlYmj8fjswAAgOBU7wEmMTFRCxcu1Jo1azR37lwdOnRIvXv31okTJ+R2u2W329WsWTOfbaKjo+V2uyVJbrfbJ7yc7T/bdz7Z2dlyOp3eJS4urn4nBgAAGox6v4Q0YMAA78/dunVTYmKiWrVqpffee09NmjSp78N5ZWVlKTMz07vu8XgIMQAABCm/P0bdrFkztW/fXvv375fL5VJ5ebmOHz/uM6akpMR7z4zL5TrnqaSz6zXdV3OWw+FQZGSkzwIAAIKT3wPMyZMndeDAAcXExKhHjx5q1KiR1q9f7+0vKipScXGxkpKSJElJSUnatWuXjh075h2Tm5uryMhIxcfH+7tcAABggHq/hPTcc89p0KBBatWqlY4cOaLJkycrNDRUDz/8sJxOp9LS0pSZmanmzZsrMjJSTz/9tJKSknTbbbdJkpKTkxUfH6/hw4dr5syZcrvdeuGFF5Seni6Hw1Hf5QIAAAPVe4D55ptv9PDDD+v777/Xtddeq9tvv11bt27VtddeK0l65ZVXFBISoiFDhqisrEwpKSl68803vduHhoZq5cqVevLJJ5WUlKSmTZtq5MiRmjZtWn2XCgAADGWzLMsKdBH+4PF45HQ6VVpayv0wCGqtJ6665LGHZ6T6sRIAuHyX+v7NdyEBAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIwTFugCAFw5rSeuuuiYwzNSr0AlAHB5OAMDAACMQ4ABAADGqfcAk52drVtvvVURERGKiorS4MGDVVRU5DPmzjvvlM1m81meeOIJnzHFxcVKTU1VeHi4oqKiNH78eFVWVtZ3uQAAwED1fg/Mpk2blJ6erltvvVWVlZX64x//qOTkZO3du1dNmzb1jhs9erSmTZvmXQ8PD/f+XFVVpdTUVLlcLm3ZskVHjx7ViBEj1KhRI02fPr2+SwYAAIap9wCzZs0an/WFCxcqKipKBQUF6tOnj7c9PDxcLperxn2sXbtWe/fu1bp16xQdHa3u3bvrxRdf1IQJEzRlyhTZ7fb6LhsAABjE7/fAlJaWSpKaN2/u056Tk6OWLVuqS5cuysrK0unTp719+fn56tq1q6Kjo71tKSkp8ng82rNnT43HKSsrk8fj8VkAAEBw8utj1NXV1Ro7dqx69eqlLl26eNuHDh2qVq1aKTY2Vjt37tSECRNUVFSkZcuWSZLcbrdPeJHkXXe73TUeKzs7W1OnTvXTTAAAQEPi1wCTnp6u3bt365NPPvFpHzNmjPfnrl27KiYmRv369dOBAwfUrl27Oh0rKytLmZmZ3nWPx6O4uLi6FQ4AABo0v11CysjI0MqVK/XRRx/p+uuvv+DYxMRESdL+/fslSS6XSyUlJT5jzq6f774Zh8OhyMhInwUAAASneg8wlmUpIyNDy5cv14YNG9SmTZuLblNYWChJiomJkSQlJSVp165dOnbsmHdMbm6uIiMjFR8fX98lAwAAw9T7JaT09HQtWrRI77//viIiIrz3rDidTjVp0kQHDhzQokWLNHDgQLVo0UI7d+7UuHHj1KdPH3Xr1k2SlJycrPj4eA0fPlwzZ86U2+3WCy+8oPT0dDkcjvouGQAAGKbez8DMnTtXpaWluvPOOxUTE+NdlixZIkmy2+1at26dkpOT1bFjRz377LMaMmSI/va3v3n3ERoaqpUrVyo0NFRJSUl65JFHNGLECJ/PjQEAAFevej8DY1nWBfvj4uK0adOmi+6nVatWWr16dX2VBQAAggjfhQQAAIzj18eoAVye1hNXBboEAGiQCDAAfNQ1NB2ekVrPlQDA+XEJCQAAGIcAAwAAjMMlJAD1gktPAK4kzsAAAADjEGAAAIBxuIQEIKBMeVScS11Aw8IZGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDl8lAFwBpnxcPgCYgjMwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAO34UE1ALfaQQADQNnYAAAgHEa9BmYOXPmaNasWXK73UpISNAbb7yhnj17Bros+AFnNgAAtdFgz8AsWbJEmZmZmjx5srZv366EhASlpKTo2LFjgS4NAAAEWIMNMLNnz9bo0aP16KOPKj4+Xm+99ZbCw8M1f/78QJcGAAACrEFeQiovL1dBQYGysrK8bSEhIerfv7/y8/Nr3KasrExlZWXe9dLSUkmSx+Op9/q6TP6w3vfZ0OyemnJFj1dddvqKHg+oLX+8lgA419m/NcuyLjiuQQaY7777TlVVVYqOjvZpj46O1hdffFHjNtnZ2Zo6deo57XFxcX6pMdg5Xw10BUDDwt8EcGWdOHFCTqfzvP0NMsDURVZWljIzM73r1dXV+uGHH9SiRQvZbLZ6O47H41FcXJy+/vprRUZG1tt+G5JgnyPzM1+wzzHY5ycF/xyZX91ZlqUTJ04oNjb2guMaZIBp2bKlQkNDVVJS4tNeUlIil8tV4zYOh0MOh8OnrVmzZv4qUZGRkUH5j/KXgn2OzM98wT7HYJ+fFPxzZH51c6EzL2c1yJt47Xa7evToofXr13vbqqurtX79eiUlJQWwMgAA0BA0yDMwkpSZmamRI0fqlltuUc+ePfXqq6/q1KlTevTRRwNdGgAACLAGG2AefPBBffvtt5o0aZLcbre6d++uNWvWnHNj75XmcDg0efLkcy5XBZNgnyPzM1+wzzHY5ycF/xyZn//ZrIs9pwQAANDANMh7YAAAAC6EAAMAAIxDgAEAAMYhwAAAAOMQYGowZ84ctW7dWo0bN1ZiYqK2bdt2SdstXrxYNptNgwcP9m+B9aC2czx+/LjS09MVExMjh8Oh9u3ba/Xq1Veo2tqr7fxeffVVdejQQU2aNFFcXJzGjRunM2fOXKFqaycvL0+DBg1SbGysbDabVqxYcdFtNm7cqJtvvlkOh0M33nijFi5c6Pc666q281u2bJnuvvtuXXvttYqMjFRSUpI+/LBhf19ZXf4bnrV582aFhYWpe/fufqvvctVlfmVlZXr++efVqlUrORwOtW7dusF+eW9d5peTk6OEhASFh4crJiZGjz32mL7//nv/F1sH2dnZuvXWWxUREaGoqCgNHjxYRUVFF91u6dKl6tixoxo3bqyuXbv6/T2CAPNPlixZoszMTE2ePFnbt29XQkKCUlJSdOzYsQtud/jwYT333HPq3bv3Faq07mo7x/Lyct199906fPiw/vrXv6qoqEh//vOfdd11113hyi9Nbee3aNEiTZw4UZMnT9a+ffs0b948LVmyRH/84x+vcOWX5tSpU0pISNCcOXMuafyhQ4eUmpqqvn37qrCwUGPHjtWoUaMa7Jt8beeXl5enu+++W6tXr1ZBQYH69u2rQYMGaceOHX6utO5qO8ezjh8/rhEjRqhfv35+qqx+1GV+DzzwgNavX6958+apqKhI7777rjp06ODHKuuutvPbvHmzRowYobS0NO3Zs0dLly7Vtm3bNHr0aD9XWjebNm1Senq6tm7dqtzcXFVUVCg5OVmnTp067zZbtmzRww8/rLS0NO3YsUODBw/W4MGDtXv3bv8VasFHz549rfT0dO96VVWVFRsba2VnZ593m8rKSus3v/mN9Ze//MUaOXKk9bvf/e4KVFp3tZ3j3LlzrbZt21rl5eVXqsTLUtv5paenW3fddZdPW2ZmptWrVy+/1lkfJFnLly+/4Jg//OEPVufOnX3aHnzwQSslJcWPldWPS5lfTeLj462pU6fWf0F+UJs5Pvjgg9YLL7xgTZ482UpISPBrXfXlUub397//3XI6ndb3339/ZYqqR5cyv1mzZllt27b1aXv99det6667zo+V1Z9jx45ZkqxNmzadd8wDDzxgpaam+rQlJiZajz/+uN/q4gzML5SXl6ugoED9+/f3toWEhKh///7Kz88/73bTpk1TVFSU0tLSrkSZl6Uuc/zggw+UlJSk9PR0RUdHq0uXLpo+fbqqqqquVNmXrC7z+81vfqOCggLvZaaDBw9q9erVGjhw4BWp2d/y8/N9fh+SlJKScsF/0yarrq7WiRMn1Lx580CXUq8WLFiggwcPavLkyYEupd598MEHuuWWWzRz5kxdd911at++vZ577jn99NNPgS6tXiQlJenrr7/W6tWrZVmWSkpK9Ne//tWY15jS0lJJuuDfVCBeZxrsJ/EGwnfffaeqqqpzPu03OjpaX3zxRY3bfPLJJ5o3b54KCwuvQIWXry5zPHjwoDZs2KBhw4Zp9erV2r9/v5566ilVVFQ0uBfTusxv6NCh+u6773T77bfLsixVVlbqiSeeaLCXkGrL7XbX+PvweDz66aef1KRJkwBV5h8vv/yyTp48qQceeCDQpdSbL7/8UhMnTtTHH3+ssLDge9k+ePCgPvnkEzVu3FjLly/Xd999p6eeekrff/+9FixYEOjyLluvXr2Uk5OjBx98UGfOnFFlZaUGDRpU60uIgVBdXa2xY8eqV69e6tKly3nHne91xu12+602zsBchhMnTmj48OH685//rJYtWwa6HL+prq5WVFSU3n77bfXo0UMPPvignn/+eb311luBLq1ebNy4UdOnT9ebb76p7du3a9myZVq1apVefPHFQJeGWlq0aJGmTp2q9957T1FRUYEup15UVVVp6NChmjp1qtq3bx/ocvyiurpaNptNOTk56tmzpwYOHKjZs2frnXfeCYqzMHv37tW//uu/atKkSSooKNCaNWt0+PBhPfHEE4Eu7aLS09O1e/duLV68ONClnCP4ovxlaNmypUJDQ1VSUuLTXlJSIpfLdc74AwcO6PDhwxo0aJC3rbq6WpIUFhamoqIitWvXzr9F11Jt5yhJMTExatSokUJDQ71tnTp1ktvtVnl5uex2u19rro26zO9Pf/qThg8frlGjRkmSunbtqlOnTmnMmDF6/vnnFRJids53uVw1/j4iIyOD6uzL4sWLNWrUKC1duvScU9kmO3HihD7//HPt2LFDGRkZkn5+nbEsS2FhYVq7dq3uuuuuAFd5eWJiYnTdddfJ6XR62zp16iTLsvTNN9/o17/+dQCru3zZ2dnq1auXxo8fL0nq1q2bmjZtqt69e+ull15STExMgCusWUZGhlauXKm8vDxdf/31Fxx7vteZ873u1gezX5nrmd1uV48ePbR+/XpvW3V1tdavX6+kpKRzxnfs2FG7du1SYWGhd7n33nu9T3vExcVdyfIvSW3nKP18+nP//v3ecCZJ//jHPxQTE9OgwotUt/mdPn36nJByNqxZQfBVYUlJST6/D0nKzc097+/DRO+++64effRRvfvuu0pNTQ10OfUqMjLynNeZJ554Qh06dFBhYaESExMDXeJl69Wrl44cOaKTJ0962/7xj38oJCTkom+cJjDtNcayLGVkZGj58uXasGGD2rRpc9FtAvI647fbgw21ePFiy+FwWAsXLrT27t1rjRkzxmrWrJnldrsty7Ks4cOHWxMnTjzv9iY8hVTbORYXF1sRERFWRkaGVVRUZK1cudKKioqyXnrppUBN4YJqO7/JkydbERER1rvvvmsdPHjQWrt2rdWuXTvrgQceCNQULujEiRPWjh07rB07dliSrNmzZ1s7duywvvrqK8uyLGvixInW8OHDveMPHjxohYeHW+PHj7f27dtnzZkzxwoNDbXWrFkTqClcUG3nl5OTY4WFhVlz5syxjh496l2OHz8eqClcVG3n+M8a+lNItZ3fiRMnrOuvv966//77rT179libNm2yfv3rX1ujRo0K1BQuqLbzW7BggRUWFma9+eab1oEDB6xPPvnEuuWWW6yePXsGagoX9OSTT1pOp9PauHGjz9/U6dOnvWP++XV08+bNVlhYmPXyyy9b+/btsyZPnmw1atTI2rVrl9/qJMDU4I033rBuuOEGy263Wz179rS2bt3q7bvjjjuskSNHnndbEwKMZdV+jlu2bLESExMth8NhtW3b1vq3f/s3q7Ky8gpXfelqM7+KigprypQpVrt27azGjRtbcXFx1lNPPWX9+OOPV77wS/DRRx9Zks5Zzs5p5MiR1h133HHONt27d7fsdrvVtm1ba8GCBVe87ktV2/ndcccdFxzfENXlv+EvNfQAU5f57du3z+rfv7/VpEkT6/rrr7cyMzN93jAbkrrM7/XXX7fi4+OtJk2aWDExMdawYcOsb7755soXfwlqmpskn9eNmt4n3nvvPat9+/aW3W63OnfubK1atcqvddr+f7EAAADG4B4YAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIzz/wCaMbgAS5/9WwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minUnitUse, maxUnitUse = 0.75, 1.25\n",
    "print(\"let's visualize over-used and underused units, <\",minUnitUse,\"or >\",maxUnitUse)\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minUnitUse:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(\"u\",unitCP[u])\n",
    "    if unitUse[u] > maxUnitUse:\n",
    "        plotPoly(unitGeom[u], 1.5)\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"and here is the histogram of HD pops relative to aDP\")\n",
    "plt.hist([HDvPop[t]/aDP for t in popHDlist],bins = [0.4, 0.6, 0.7, 0.85, 0.9, 0.95,\n",
    "         0.98, 1.0, 1.02, 1.05, 1.1, 1.15, 1.3, 1.5, 2.0])\n",
    "plt.axvline(1.0,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "9bc31b5a-992d-4a38-b9fe-3470b55e1e2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"opt3ContigButOffPop.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6150cac-2249-415b-a364-edf55dc441ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "114705bb-9640-45f1-9f25-030030efaa63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#below here -- working on tightening use distro while squaring up pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "5d7d8519-eb3d-4345-9ac6-ab888e61c348",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 1.01395 0.11265\n"
     ]
    }
   ],
   "source": [
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        latestHDpop[t] += unitPop[u]\n",
    "        latestUnitUse[u] += nDistricts * HDweight[t]\n",
    "latestUnitWeights, latestUnitDistro = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b1971853-bda7-49a4-9f25-1bdfd5e75aa2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 800\n",
      "working on avg unit-to-HDcp dist for HD 1600\n",
      "working on avg unit-to-HDcp dist for HD 2400\n",
      "working on avg unit-to-HDcp dist for HD 3200\n",
      "working on avg unit-to-HDcp dist for HD 4000\n"
     ]
    }
   ],
   "source": [
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "f5b99900-614a-47df-b7de-5d59fffd6d3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 1.01395 0.11265\n"
     ]
    }
   ],
   "source": [
    "#Restart - may want to comment out first line\n",
    "HDunitList = [latestHDlist[t].copy() for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits) ]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, weights = unitPop,bins = 50)\n",
    "plt.show()\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()\n",
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cea58fc2-c389-474a-9800-0f4906640a27",
   "metadata": {},
   "outputs": [],
   "source": [
    "#WA - OPTIONAL restart here, pull in file*********************\n",
    "print(\"this optional RESTART block pulls in an existing UNIT (not vtd) list for squaring up\") \n",
    "print(\"Must have already established unitGeoms, pops, topology in above blocks\")\n",
    "guessedFile = \"enter the unpatched UNIT list file, e.g. ./2024state_HD_output/\"+STATE+str(nHDs)+\"opt3ContigButOffPopUnit.csv\"\n",
    "infile = input(guessedFile)\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDunitList\"]  #inDF[\"HDvtdList\"]\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "sumWt = np.sum(HDweight)\n",
    "print(\"sum of HDweight should be unity, actually is\",sumWt)\n",
    "print(\"I will now renormalize\")\n",
    "HDweight = [HDweight[t] /sumWt for t in range(nHDs)]\n",
    "print(\"normalized weight is now\",np.sum(HDweight))\n",
    "HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of units\") #vtds\")\n",
    "HDunitList = [inVTDlist[t].copy() for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits) ]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse,weights = unitPop)\n",
    "print(\"weighted unit use histogram\")\n",
    "plt.show()\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "27b51cd0-8bb6-4b16-bb79-37498e59ce5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now square up the 527 HDs with pop < 761670 to within 882\n",
      "working on squaring up HD 22 time is now 0\n",
      "working on squaring up HD 699 time is now 130\n",
      "working on squaring up HD 1523 time is now 276\n",
      "working on squaring up HD 2059 time is now 606\n",
      "working on squaring up HD 2805 time is now 654\n",
      "working on squaring up HD 4435 time is now 750\n",
      "We have addressed underpop in a total of 527 HDs\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING UP UNDERPOPPED\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "underPoppedList = list()\n",
    "minDistrictPop, maxDistrictPop = 0.99 * aDP, 1.01 * aDP\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop < minDistrictPop and t in popHDlist:\n",
    "        underPoppedList.append(t)\n",
    "startTime = time.time()\n",
    "maxGap = 0.9 * np.median(unitPop)\n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) ) \n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)  #NOTE - HAVE NOT MOVED TO AVOIDEDENCLAVE HERE (YET)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "bc01409d-d9e4-43a6-9fae-c76aa7bdc855",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amped unit avg and sd usage are 1.02092 0.11016\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#prevUnitUse = [0.]*nUnits\n",
    "#for t in popHDlist:\n",
    "#    for u in latestHDlist[t]:\n",
    "#        prevUnitUse[u] += HDweight[t] * nDistricts\n",
    "\n",
    "ampedUnitUse = [0. for u in range(nUnits)]\n",
    "ampedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "ampedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in ampedHDlist[t]:\n",
    "        ampedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "#plt.hist(prevUnitUse,bins=50,weights=unitPop,label=\"previous\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.legend()\n",
    "ampedUnitUseAvg, ampedUnitUseSD = getWeightedAvgAndSD(ampedUnitUse,unitPop)\n",
    "#print(\"previous unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ee512b2b-4daa-4a6b-9452-e8dfdfdebc88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the current barredJettisonSet (usually CCB's) is {4019, 4020, 4021, 4022}\n",
      "4019 0.980540910976334\n",
      "4020 1.0426324466324965\n",
      "4021 0.9807202798804916\n",
      "4022 0.9824073873993059\n"
     ]
    }
   ],
   "source": [
    "#CHECK ON BARRED JETTISON SET BEFORE RUNNING BELOW\n",
    "print(\"the current barredJettisonSet (usually CCB's) is\",barredJettisonSet)\n",
    "for u in barredJettisonSet:\n",
    "    print(u,unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "5a6cb84c-1d0f-4f02-831c-985fe4078dc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{4019, 4021, 4022} {4019, 4020, 4021, 4022}\n"
     ]
    }
   ],
   "source": [
    "origBarredJettisonSet = set(list(barredJettisonSet))\n",
    "for u in origBarredJettisonSet:\n",
    "    if unitUse[u] > 1.04:\n",
    "        barredJettisonSet.remove(u)  #this one is overused in this state\n",
    "print(barredJettisonSet, origBarredJettisonSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "dd7d9818-344c-4150-917b-57a4ee051c92",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\n",
      "Let's now square down the 1087 overPopped HDs to within 882\n",
      "working on squaring down HD 0 time is now 0\n",
      "working on squaring down HD 131 time is now 5\n",
      "working on squaring down HD 342 time is now 8\n",
      "working on squaring down HD 507 time is now 14\n",
      "working on squaring down HD 625 time is now 18\n",
      "working on squaring down HD 763 time is now 22\n",
      "working on squaring down HD 870 time is now 25\n",
      "working on squaring down HD 955 time is now 29\n",
      "working on squaring down HD 1079 time is now 34\n",
      "working on squaring down HD 1474 time is now 49\n",
      "working on squaring down HD 1607 time is now 57\n",
      "working on squaring down HD 1716 time is now 62\n",
      "working on squaring down HD 1892 time is now 66\n",
      "working on squaring down HD 2066 time is now 70\n",
      "working on squaring down HD 2346 time is now 74\n",
      "working on squaring down HD 2721 time is now 81\n",
      "working on squaring down HD 3554 time is now 87\n",
      "working on squaring down HD 4436 time is now 97\n",
      "working on squaring down HD 4592 time is now 104\n",
      "We have addressed overpop in a total of 1087 HDs\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING DOWN  #new for VA, the barred set is dynamic; we stop shedding upon underuse. \n",
    "#   now with avoidedIsland timesaver\n",
    "print(\"Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\")\n",
    "#HDunitList = [ampedHDlist[t].copy() for t in range(nHDs)]\n",
    "#HDvPop =     [ampedHDpop[t]         for t in range(nHDs)]\n",
    "#unitUse =    [ampedUnitUse[u]       for u in range(nUnits)] #saving in case I need to re-run\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList = list()\n",
    "startTime = time.time()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "maxGap = 0.9 * np.median(unitPop)   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))  \n",
    "barredSet = set(list(barredJettisonSet))\n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    for u in origBarredJettisonSet:\n",
    "        if unitUse[u] < 1.00:\n",
    "            barredSet = barredSet.union({u})  #adding back in when become underused (new for VA)\n",
    "    if ii%60 == 0:\n",
    "        print(\"working on squaring down HD\",t,\"time is now\",int(time.time()-startTime))\n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t],unitNbrs)\n",
    "    if not (unbroken and noEnclave):\n",
    "        print(\"SKIPPING shedding attempt on overpopped HD\",t,\"with pop\",HDvPop[t],\"because it was not legal to start\")\n",
    "    else:\n",
    "        #avgDist = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        excess = HDvPop[t] - aDP\n",
    "        origExcess = excess\n",
    "        HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "        #barredSet = barredJettisonSet  #see above for VA-on modification\n",
    "        #barredSet = set(get2nbrs([starterU],unitNbrs)).union(barredJettisonSet)  #weaker specification - must be close to be barred\n",
    "\n",
    "        for u in set(HDunitList[t]).difference(barredSet):\n",
    "            isBoundary = False\n",
    "            for uu in unitNbrs[u]:\n",
    "                if uu not in HDunitList[t]:\n",
    "                    isBoundary = True\n",
    "                    break\n",
    "            if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "                HDboundaryList.append(u)\n",
    "                HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "        currList = HDunitList[t].copy()\n",
    "        shedList, stillGoing = list(), True\n",
    "        while excess > maxGap and len(HDboundaryList) > 0 and stillGoing:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "            while i < len(HDboundaryScore) and notYetPicked and stillGoing:        \n",
    "                listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "                unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "                tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "                contig = avoidedIsland(tryList, unitNbrs, [unitNoToShed] )  #new for MO - faster contig check\n",
    "                #unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "                #if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                if contig:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't drop any more units without creating an enclave\n",
    "                print(\"can't drop any more units to HD\",t,\"without creating an enclave\")\n",
    "            else: #add this eligible unit\n",
    "                shedList.append(unitNoToShed)\n",
    "                excess -= unitPop[unitNoToShed]        \n",
    "                del currList[currList.index(unitNoToShed) ]\n",
    "                del HDboundaryList[listNo]\n",
    "                del HDboundaryScore[listNo]\n",
    "                for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                    if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and\n",
    "                                                                      u not in barredSet): \n",
    "                        HDboundaryList.append(u)\n",
    "                        HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "                for uu in HDboundaryList.copy():\n",
    "                    if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                        del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                        del HDboundaryList[HDboundaryList.index(uu)]   \n",
    "        for u in shedList:\n",
    "            unitUse[u] -= HDweight[t] * nDistricts\n",
    "        HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "                 np.sum([unitPop[u] for u in shedList]) )\n",
    "\n",
    "        HDnShedUnits[t] = -1 * len(shedList)\n",
    "\n",
    "print(\"We have addressed overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c4c929d-0208-4785-aa15-5681a493a530",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "9a19b23e-43cc-4609-9ee7-6d1057a7a9e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the stats prior to any equi-pop patching\n",
      "amped unit avg and sd usage are 1.02092 0.11016\n",
      "shed unit avg and sd usage are 1.0007 0.10046\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here are the final populations\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 4604\n",
      "working on HD 600 out of 4604\n",
      "working on HD 1200 out of 4604\n",
      "working on HD 1800 out of 4604\n",
      "working on HD 2400 out of 4604\n",
      "working on HD 3000 out of 4604\n",
      "working on HD 3600 out of 4604\n",
      "working on HD 4200 out of 4604\n",
      "all done checking HD and complement contiguity for all 4604 HDs\n",
      "underpop contigFail, enclaveFail = 0 0 out of 527\n",
      "overpop contigFail, enclaveFail = 0 0 out of 1087\n",
      "total contigFail, enclaveFail =  0 0\n",
      "CCB unit 4019 with pop 161018.0 now has use 0.98054\n",
      "CCB unit 4020 with pop 185562.0 now has use 1.03409\n",
      "CCB unit 4021 with pop 139686.0 now has use 0.98072\n",
      "CCB unit 4022 with pop 25126.0 now has use 0.98241\n"
     ]
    }
   ],
   "source": [
    "print(\"Here are the stats prior to any equi-pop patching\")\n",
    "shedUnitUse = [0. for u in range(nUnits)]\n",
    "shedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "shedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in shedHDlist[t]:\n",
    "        shedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "#plt.hist(latestUnitUse,bins=50,weights=unitPop,label=\"pre-squaring\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.hist(shedUnitUse, bins=50, weights=unitPop,label=\"shed\",histtype=\"step\")\n",
    "plt.legend()\n",
    "#latestAvg, latestSD = getWeightedAvgAndSD(latestUnitUse,unitPop)\n",
    "shedUnitUseAvg, shedUnitUseSD = getWeightedAvgAndSD(shedUnitUse,unitPop)\n",
    "#print(\"orig unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "print(\"shed unit avg and sd usage are\", r5(shedUnitUseAvg),  r5(shedUnitUseSD) )\n",
    "plt.show()\n",
    "# note: when I ran this early Jan, went from 0.12662 to 0.09961 to 0.09432 SD orig-amped-shed, but contig not yet done\n",
    "print(\"and here are the final populations\")\n",
    "plt.hist([shedHDpop[t] for t in popHDlist],bins=20)\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "failEnclaveSet, failContigSet = set(), set()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%600 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs,5)\n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        #print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "        pass\n",
    "    if not unbroken:\n",
    "        failContigSet.add(t)\n",
    "    if not noEnclave:\n",
    "        failEnclaveSet.add(t)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "failContigUnderpopped, failEnclaveUnderpopped = set(underPoppedList).intersection(failContigSet), set(underPoppedList).intersection(failEnclaveSet)\n",
    "failContigOverpopped, failEnclaveOverpopped = set(overPoppedList).intersection(failContigSet), set(overPoppedList).intersection(failEnclaveSet)\n",
    "print(\"underpop contigFail, enclaveFail =\",len(failContigUnderpopped), len(failEnclaveUnderpopped),\"out of\",len(underPoppedList))\n",
    "print(\"overpop contigFail, enclaveFail =\",len(failContigOverpopped), len(failEnclaveOverpopped),\"out of\",len(overPoppedList))\n",
    "print(\"total contigFail, enclaveFail = \",len(failContigSet), len(failEnclaveSet) )\n",
    "for u in range(nUnits):\n",
    "    if abs( allUnits[u]%1 - 0.25) < 0.01:\n",
    "        print(\"CCB unit\",u,\"with pop\",unitPop[u],\"now has use\",r5(unitUse[u]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ff3288f1-f9ca-473c-886c-ee8d6c2637bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4604 4023\n"
     ]
    }
   ],
   "source": [
    "print(nHDs, nUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ed668f8-bf1e-46eb-963f-cf5e2b935b11",
   "metadata": {},
   "outputs": [],
   "source": [
    "#see MD code if we need to remove a stuck CCB's from any HDs and then redo the square-up after dropping them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e7bfa674-8fc8-42c8-86e9-76d334d3c47c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"opt3ContigGoodPop.csv\" #\"contigUnpatchedB.csv\" unpatchedContigGoodPop.csv\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "c0c3a3d8-cf79-40ad-aa2e-b6793266f1d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    }
   ],
   "source": [
    "#SKIP THIS FOR STATES WITH FRAGMENTED VTD'S ###########\n",
    "print(\"prior to patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        for i,uu in enumerate(surrounders):\n",
    "            if u == uu:\n",
    "                unitVTDlist[u].append(surroundedVTDs[i])\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        vtdList[t] += unitVTDlist[u]\n",
    "    #add more stuff here to convert to vtd lists\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"needsEnclaveRemoval.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "7a36c0ba-5a68-445b-a223-b7fa55dfd728",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 4604 HD centers\n"
     ]
    }
   ],
   "source": [
    "nHDs = len(vtdGeom)\n",
    "print(\"there are\",nHDs,\"HD centers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "3944dd16-da20-4f60-8fc8-30571e246cda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "orig unit avg and sd usage are 1.0007 0.10046\n"
     ]
    }
   ],
   "source": [
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fc7131e-4fa8-4152-8e3c-45f92cc54ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\") #vtdlist for patching\")\n",
    "print(\"Must have already established unitGeoms, pops, topology -- or read in via next block\")\n",
    "guessedFile = \"enter the unpatched UNIT list file, e.g. ./2024state_HD_output/\"+STATE+str(nHDs)+\"opt3ContigGoodPop.csv\"\n",
    "infile = input(guessedFile)\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDunitList\"]  #inDF[\"HDvtdList\"]\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "sumWt = np.sum(HDweight)\n",
    "print(\"sum of HDweight should be unity, actually is\",sumWt)\n",
    "print(\"I will now renormalize\")\n",
    "HDweight = [HDweight[t] /sumWt for t in range(nHDs)]\n",
    "print(\"normalized weight is now\",np.sum(HDweight))\n",
    "HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of units\") #vtds\")\n",
    "HDunitList = [inVTDlist[t].copy() for t in range(nHDs)]\n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18581a7b-052f-413c-8312-0c569c0aba15",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SKIP THE BELOW IF NOT A COLD RESTART\n",
    "print(\"upon restart, need to pull in unit topology and data\")  #note, this won't have geometries for plotting, but fine for patching\n",
    "topologyFile = \"enter the unpatched UNIT list file, e.g. ./state_map_files/MNunitTopologies_06Apr24.csv\"\n",
    "infile = input(topologyFile)\n",
    "topDF = pd.read_csv(infile)\n",
    "unitCPx, unitCPy, unitPop = topDF[\"centroid x\"], topDF[\"centroid y\"], topDF[\"unitPop\"]\n",
    "nUnits = len(unitPop)\n",
    "unitCP = [Point(unitCPx[u], unitCPy[u]) for u in range(nUnits) ]\n",
    "onBorder = topDF[\"onBorder\"]\n",
    "borderSet = set()\n",
    "for i, onB in enumerate(onBorder):\n",
    "    if onB == 1:\n",
    "        borderSet.add(i)\n",
    "borderList = list(borderSet)\n",
    "\n",
    "nbrListString = topDF[\"neighborList\"]\n",
    "unitNbrs = [ast.literal_eval(nbrListString[u]) for u in range(nUnits)]\n",
    "unitParentVTDno = topDF[\"unitParentVTDno\"]  #NOTE: some units are VTD fragments, so they share a parentVTDno\n",
    "uVLstring = topDF[\"unitVTDlist\"]\n",
    "unitNbrs = [ast.literal_eval(uVLstring[u]) for u in range(nUnits)]\n",
    "allUnits = topDF[\"allUnits\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1acd746d-f3e7-4d23-992a-cae9efbf040d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#needed for restart only  about 5sec per 2000 HDs\n",
    "avgDist = [0. for t in range(nHDs)]\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if t%800 == 0:\n",
    "        print(\"working avg precinct-to-HDcP distance for HD\",t)\n",
    "    if HDvPop[t] > 0.1 * aDP:\n",
    "        avgDist[t] = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        popHDlist.append(t)\n",
    "print(\"all avgDist to HD centers computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b0b39d6-e539-4fb4-a9bf-cc0c4369f431",
   "metadata": {},
   "outputs": [],
   "source": [
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()  #optional contiguity check\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"checking for discontiguities for HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems.  Should all be zeroes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "27b42f93-dc87-4afe-8eb3-f30fdff4a737",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking unitUse, pop for county clusters\n",
      "4019 0.98054 161018.0\n",
      "4020 1.03409 185562.0\n",
      "4021 0.98072 139686.0\n",
      "4022 0.98241 25126.0\n"
     ]
    }
   ],
   "source": [
    "print(\"checking unitUse, pop for county clusters\")\n",
    "for uNo in allUnits:\n",
    "    if uNo - int(uNo) > 0.23 and uNo - int(uNo) < 0.27:\n",
    "        u = allUnits.index(uNo)\n",
    "        print(u,r5(unitUse[u]), unitPop[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "72941b8f-7be6-4a31-9377-618f44a34336",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unpatchedUnitUse = unitUse.copy()              #... for safekeeping\n",
    "unpatchedHDlist =  [HDunitList[t].copy() for t in range(nHDs) ]\n",
    "unpatchedHDpop =   HDvPop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "5f9ca091-1eb6-4135-a233-7f5ae0f5e3d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unitUse = unpatchedUnitUse.copy()              #... for restarting\n",
    "HDunitList =  [unpatchedHDlist[t].copy() for t in range(nHDs) ]\n",
    "HDvPop =   unpatchedHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc95ca58-c27f-4ac9-b6f7-234a94945f07",
   "metadata": {},
   "outputs": [],
   "source": [
    "#oops, for MO, I should have skipped the below, gone straight to the WI-improved code using avoidedEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "e9dd457d-fb37-49cf-9b19-19c17c328ede",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we exchange in under-used, THEN shed over-used\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.07\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.06\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this would currently cover a total of 847 units\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated number of units to try boosting usage 200\n",
      "enter updated stopMinUse value for ending usage boost on a unit; I reco 0.97 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 1723 units out of 4023 with usage below 0.98\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n",
      "enter 1 to print out stats for every patched HD, otherwise enter reporting frequency; e.g. 10 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's further tighten the distro with exchanges up to 38468\n",
      "current avg and SD of unit usage are 1.0007 0.10046 . Now trying to increase up to 200 units' underusage\n",
      "all done trying to increase usage of unit 335 final usage = 0.96863 544 sec elapsed 184 13 successful, failed patches, couldn't start= 52\n",
      "current avg and SD of usage are 1.00069 0.09593\n",
      "all done trying to increase usage of unit 317 final usage = 0.73946 763 sec elapsed 30 3 successful, failed patches, couldn't start= 80\n",
      "current avg and SD of usage are 1.00069 0.09498\n",
      "all done trying to increase usage of unit 39 final usage = 0.98196 1614 sec elapsed 126 21 successful, failed patches, couldn't start= 45\n",
      "current avg and SD of usage are 1.00068 0.09199\n",
      "all done trying to increase usage of unit 329 final usage = 0.93016 2305 sec elapsed 127 24 successful, failed patches, couldn't start= 30\n",
      "current avg and SD of usage are 1.00068 0.08989\n",
      "all done trying to increase usage of unit 338 final usage = 0.98089 3559 sec elapsed 122 16 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00068 0.08823\n",
      "all done trying to increase usage of unit 1096 final usage = 0.76257 3684 sec elapsed 13 2 successful, failed patches, couldn't start= 73\n",
      "current avg and SD of usage are 1.00068 0.0881\n",
      "all done trying to increase usage of unit 332 final usage = 0.89693 4358 sec elapsed 84 21 successful, failed patches, couldn't start= 65\n",
      "current avg and SD of usage are 1.00068 0.08706\n",
      "all done trying to increase usage of unit 300 final usage = 0.82517 4794 sec elapsed 36 8 successful, failed patches, couldn't start= 84\n",
      "current avg and SD of usage are 1.00067 0.08661\n",
      "all done trying to increase usage of unit 302 final usage = 0.9176 5595 sec elapsed 89 17 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 1.00068 0.08568\n",
      "begin usage increase for unit 311 with usage 0.76217 . Sec, total UU units tried = 5596 10\n",
      "all done trying to increase usage of unit 311 final usage = 0.82565 6284 sec elapsed 38 27 successful, failed patches, couldn't start= 68\n",
      "current avg and SD of usage are 1.00068 0.08541\n",
      "all done trying to increase usage of unit 45 final usage = 0.98349 7490 sec elapsed 99 21 successful, failed patches, couldn't start= 27\n",
      "current avg and SD of usage are 1.00067 0.08439\n",
      "all done trying to increase usage of unit 40 final usage = 1.01314 8415 sec elapsed 92 11 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00067 0.08316\n",
      "all done trying to increase usage of unit 1103 final usage = 0.8081 8518 sec elapsed 15 2 successful, failed patches, couldn't start= 47\n",
      "current avg and SD of usage are 1.00067 0.08301\n",
      "all done trying to increase usage of unit 32 final usage = 0.98065 8695 sec elapsed 92 0 successful, failed patches, couldn't start= 44\n",
      "current avg and SD of usage are 1.00064 0.0793\n",
      "all done trying to increase usage of unit 42 final usage = 0.98209 9814 sec elapsed 89 14 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00065 0.07867\n",
      "all done trying to increase usage of unit 58 final usage = 0.98017 10697 sec elapsed 88 11 successful, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 1.00064 0.07753\n",
      "all done trying to increase usage of unit 50 final usage = 0.98053 11939 sec elapsed 73 0 successful, failed patches, couldn't start= 29\n",
      "current avg and SD of usage are 1.00065 0.07722\n",
      "all done trying to increase usage of unit 333 final usage = 0.80002 12064 sec elapsed 8 5 successful, failed patches, couldn't start= 148\n",
      "current avg and SD of usage are 1.00065 0.07715\n",
      "all done trying to increase usage of unit 325 final usage = 0.8252 12493 sec elapsed 39 23 successful, failed patches, couldn't start= 101\n",
      "current avg and SD of usage are 1.00065 0.07684\n",
      "begin usage increase for unit 1110 with usage 0.79827 . Sec, total UU units tried = 12494 20\n",
      "all done trying to increase usage of unit 1110 final usage = 0.93867 13526 sec elapsed 82 5 successful, failed patches, couldn't start= 97\n",
      "current avg and SD of usage are 1.00065 0.07638\n",
      "all done trying to increase usage of unit 669 final usage = 0.98391 13758 sec elapsed 63 0 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00064 0.07389\n",
      "all done trying to increase usage of unit 59 final usage = 0.98113 15296 sec elapsed 88 7 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00065 0.07341\n",
      "all done trying to increase usage of unit 1097 final usage = 0.83704 16041 sec elapsed 24 0 successful, failed patches, couldn't start= 84\n",
      "current avg and SD of usage are 1.00065 0.07331\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[114], line 104\u001b[0m\n\u001b[0;32m    102\u001b[0m listNo \u001b[38;5;241m=\u001b[39m idx[ij]   \u001b[38;5;66;03m#work from low to high score\u001b[39;00m\n\u001b[0;32m    103\u001b[0m addU \u001b[38;5;241m=\u001b[39m addCandidates[listNo]\n\u001b[1;32m--> 104\u001b[0m cContig \u001b[38;5;241m=\u001b[39m \u001b[43mwontEnclave\u001b[49m\u001b[43m(\u001b[49m\u001b[43maddU\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrySet\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munitNbrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mborderUnits\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m#4/20/24 sub this in as faster? vs below line\u001b[39;00m\n\u001b[0;32m    105\u001b[0m \u001b[38;5;66;03m#contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)  #4/20/24 this is unnec slow\u001b[39;00m\n\u001b[0;32m    106\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cContig: \u001b[38;5;66;03m#contig and cContig:\u001b[39;00m\n",
      "Cell \u001b[1;32mIn[2], line 877\u001b[0m, in \u001b[0;36mwontEnclave\u001b[1;34m(proposedU, dList, NBRLIST, mapBDRYLIST)\u001b[0m\n\u001b[0;32m    875\u001b[0m adjSet \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(ADJLIST)  \u001b[38;5;66;03m#align nomenclature w enclaveCheck\u001b[39;00m\n\u001b[0;32m    876\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m UU \u001b[38;5;129;01min\u001b[39;00m ADJLIST:\n\u001b[1;32m--> 877\u001b[0m     adjSet \u001b[38;5;241m=\u001b[39m adjSet\u001b[38;5;241m.\u001b[39munion( \u001b[38;5;28;43mset\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mNBRLIST\u001b[49m\u001b[43m[\u001b[49m\u001b[43mUU\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdifference\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mset\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnewList\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m )\n\u001b[0;32m    878\u001b[0m ADJLIST \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(adjSet)\n\u001b[0;32m    879\u001b[0m wontEnclave, __ \u001b[38;5;241m=\u001b[39m   isContiguous(ADJLIST, NBRLIST)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "#FIRST PATCHING BLOCK - boosting underuse.  Suppresses long strings.  For some states (e.g. MA), do overused first. MA:over-und-over-und\n",
    "print(\"Here, we exchange in under-used, THEN shed over-used\")\n",
    "maxSD = 0.07  #0.07  #0.05   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "#print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = float(input(\"enter updated stopMinUse value for ending usage boost on a unit; I reco 0.97\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "nSmallUsers = 5\n",
    "maxExchangePop = 0.05*aDP \n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP\n",
    "debug1 = int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency; e.g. 10\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "\n",
    "attemptedSmallUs, startTime = list(), time.time()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    #each round, find the (5) most underused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nSmallFound, smallUsers = 0,0,list()\n",
    "    while nSmallFound < nSmallUsers:\n",
    "        consideredSmallU = idx[idxNo]\n",
    "        if consideredSmallU not in attemptedSmallUs:\n",
    "            smallUsers.append(consideredSmallU)\n",
    "            nSmallFound +=1\n",
    "        idxNo +=1\n",
    "    UUUclusters = [ [b] + unitNbrs[b] for b in smallUsers ]\n",
    "    smallUnderUse = [np.sum([(unitUse[j] - 1.) for j in UUUclusters[i] ]) for i in range(nSmallUsers) ]\n",
    "    smallI = smallUnderUse.index(np.max(smallUnderUse))\n",
    "    UUU = smallUsers[ smallI ]  #pick the unit that centers cluster with least composite use\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    UUUc = UUUclusters[smallI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"begin usage increase for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Sec, total UU units tried =\",\n",
    "              int(time.time()-startTime),len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "        idx0 = np.argsort(uuuDists)\n",
    "    hasCandidates = True\n",
    "    if len(uuuDists) == 0:  #this underused unit is buried inside others; skip it\n",
    "        hasCandidates = False\n",
    "        print(\"   Couldn't find any HDs adjacent to underused unit\",UUU)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs) and hasCandidates: #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig): #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    cContig = wontEnclave(addU, list(trySet), unitNbrs, borderUnits)  #4/20/24 sub this in as faster? vs below line\n",
    "                    #contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)  #4/20/24 this is unnec slow\n",
    "                    if cContig: #contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying to increase usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "6260856a-eab5-45c5-9abc-236771b7ae72",
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"opt3partialUnderPatch.csv\" #\"contigUnpatchedB.csv\" unpatchedContigGoodPop.csv\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20bc97ce-3dd3-4394-b38a-06531086355a",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"oops, I should have not run the above, but started with the below faster block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "fd36caf6-71e4-44a4-9d09-7469512c5e05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we exchange in under-used, THEN shed over-used.  Using new-for-WI avoidedEnclave routines\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.07\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.06\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this would currently cover a total of 659 units\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated number of units to try boosting usage 100\n",
      "enter updated stopMinUse value for ending usage boost on a unit; I reco 0.97 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 1326 units out of 4023 with usage below 0.98\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n",
      "enter 1 to print out stats for every patched HD, otherwise enter reporting frequency; e.g. 10 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's further tighten the distro with exchanges up to 38468\n",
      "current avg and SD of unit usage are 1.00064 0.07064 . Now trying to increase up to 100 units' underusage\n",
      "all done trying to increase usage of unit 1096 final usage = 0.82074 94 sec elapsed 9 80 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00064 0.07058\n",
      "all done trying to increase usage of unit 365 final usage = 0.98005 152 sec elapsed 89 4 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00065 0.06823\n",
      "all done trying to increase usage of unit 317 final usage = 0.8604 229 sec elapsed 33 24 successful, failed patches, couldn't start= 76\n",
      "current avg and SD of usage are 1.00065 0.06806\n",
      "all done trying to increase usage of unit 47 final usage = 0.98245 373 sec elapsed 67 0 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00065 0.06773\n",
      "all done trying to increase usage of unit 2069 final usage = 0.98293 439 sec elapsed 79 78 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00064 0.06564\n",
      "all done trying to increase usage of unit 677 final usage = 0.98181 497 sec elapsed 56 22 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00062 0.06378\n",
      "all done trying to increase usage of unit 2074 final usage = 0.98331 548 sec elapsed 86 48 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00063 0.06193\n",
      "all done trying to increase usage of unit 300 final usage = 0.97664 679 sec elapsed 88 23 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00063 0.06149\n",
      "all done trying to increase usage of unit 1104 final usage = 0.87874 782 sec elapsed 34 39 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00063 0.06131\n",
      "begin usage increase for unit 312 with usage 0.82565 . Sec, total UU units tried = 783 10\n",
      "all done trying to increase usage of unit 312 final usage = 0.97434 917 sec elapsed 70 42 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00063 0.06093\n",
      "all done trying to increase usage of unit 1275 final usage = 0.90866 1036 sec elapsed 35 86 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00063 0.0603\n",
      "all done trying to increase usage of unit 1101 final usage = 0.92087 1160 sec elapsed 63 22 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00063 0.06009\n",
      "all done trying to increase usage of unit 1095 final usage = 0.86285 1221 sec elapsed 21 18 successful, failed patches, couldn't start= 54\n",
      "current avg and SD of usage are 1.00063 0.06005\n",
      "all done trying to increase usage of unit 1187 final usage = 0.98069 1311 sec elapsed 61 19 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00063 0.05854\n"
     ]
    }
   ],
   "source": [
    "#NEW for WI - faster(?) alternative to above, use the avoidedEnclave, avoidedIsland subroutines vs. wontEnclave\n",
    "\n",
    "#FIRST PATCHING BLOCK - boosting underuse.  Suppresses long strings.  For some states (e.g. MA), do overused first. MA:over-und-over-und\n",
    "print(\"Here, we exchange in under-used, THEN shed over-used.  Using new-for-WI avoidedEnclave routines\")\n",
    "maxSD = 0.07  #0.07  #0.05   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "#print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = float(input(\"enter updated stopMinUse value for ending usage boost on a unit; I reco 0.97\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "nSmallUsers = 5\n",
    "maxExchangePop = 0.05*aDP \n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP\n",
    "debug1 = int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency; e.g. 10\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "\n",
    "attemptedSmallUs, startTime = list(), time.time()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    #each round, find the (5) most underused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nSmallFound, smallUsers = 0,0,list()\n",
    "    while nSmallFound < nSmallUsers:\n",
    "        consideredSmallU = idx[idxNo]\n",
    "        if consideredSmallU not in attemptedSmallUs:\n",
    "            smallUsers.append(consideredSmallU)\n",
    "            nSmallFound +=1\n",
    "        idxNo +=1\n",
    "    UUUclusters = [ [b] + unitNbrs[b] for b in smallUsers ]\n",
    "    smallUnderUse = [np.sum([(unitUse[j] - 1.) for j in UUUclusters[i] ]) for i in range(nSmallUsers) ]\n",
    "    smallI = smallUnderUse.index(np.max(smallUnderUse))\n",
    "    UUU = smallUsers[ smallI ]  #pick the unit that centers cluster with least composite use\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    UUUc = UUUclusters[smallI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"begin usage increase for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Sec, total UU units tried =\",\n",
    "              int(time.time()-startTime),len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "        idx0 = np.argsort(uuuDists)\n",
    "    hasCandidates = True\n",
    "    if len(uuuDists) == 0:  #this underused unit is buried inside others; skip it\n",
    "        hasCandidates = False\n",
    "        print(\"   Couldn't find any HDs adjacent to underused unit\",UUU)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs) and hasCandidates: #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        complementContig = avoidedEnclave(HDunitList[t], unitNbrs, UUUc)\n",
    "        #contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not complementContig: #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            complementContig  = avoidedEnclave(HDunitList[t], unitNbrs, [UUU])\n",
    "            loopUUUc = [UUU]\n",
    "        if complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    cContig = avoidedEnclave(list(trySet), unitNbrs, [addU])\n",
    "                    #cContig = wontEnclave(addU, list(trySet), unitNbrs, borderUnits)  #4/20/24 sub this in as faster? vs below line\n",
    "                    #contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)  #4/20/24 this is unnec slow\n",
    "                    if cContig: #contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        cContig = avoidedIsland(list(trySet), unitNbrs, [shedU])\n",
    "                        #contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if cContig: # and contig :\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying to increase usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "861f29d3-b727-49af-90a8-aa9a963e0741",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.0007 1.00063 and their SDs are 0.10046 0.05854\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 4604\n",
      "working on HD 500 out of 4604\n",
      "working on HD 1000 out of 4604\n",
      "working on HD 1500 out of 4604\n",
      "working on HD 2000 out of 4604\n",
      "working on HD 2500 out of 4604\n",
      "working on HD 3000 out of 4604\n",
      "working on HD 3500 out of 4604\n",
      "working on HD 4000 out of 4604\n",
      "working on HD 4500 out of 4604\n",
      "all done checking HD and complement contiguity for all 4604 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "0f593194-4a74-46e1-85aa-7be50e752f8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "oops, error in HDvPop, let's fix\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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+oJqjR49q3Lhx8nq9io6O1sSJE3XixImgml27dmngwIEKDw9XYmKiFixYUPfuAABASKpzgDl58qT69u2rJUuW1Dq/YMECPfnkk/rd736nbdu2qXXr1kpNTdXXX3/t1IwbN05FRUXKycnR2rVrlZeXp8mTJzvzgUBAKSkp6tSpkwoKCrRw4ULNmTNHzzzzzAW0CAAAQo3LGGMu+GCXS3/961918803S/q/uy8JCQmaNm2aHnroIUlSeXm54uLitGLFCt12221699131bNnT7311lu65pprJEkbNmzQiBEj9NFHHykhIUFPP/20fv3rX8vv98vtdkuSHn74Ya1Zs0bvvffed1pbIBBQVFSUysvL5fV6L7RF4KLQ+eF1Tb2Ei8Kh+elNvQSg2fuuv7/r9RmYgwcPyu/3a9iwYc5YVFSUkpKSlJ+fL0nKz89XdHS0E14kadiwYQoLC9O2bducmkGDBjnhRZJSU1NVXFysY8eO1XrtiooKBQKBoA0AAISmeg0wfr9fkhQXFxc0HhcX58z5/X7FxsYGzbds2VLt2rULqqntHGdf45uys7MVFRXlbImJid+/IQAA0CyFzKeQsrKyVF5e7myHDx9u6iUBAIAGUq8BxufzSZLKysqCxsvKypw5n8+nI0eOBM1XVVXp6NGjQTW1nePsa3yTx+OR1+sN2gAAQGiq1wDTpUsX+Xw+bdq0yRkLBALatm2bkpOTJUnJyck6fvy4CgoKnJrNmzerurpaSUlJTk1eXp4qKyudmpycHHXr1k1t27atzyUDAAAL1TnAnDhxQoWFhSosLJT0fw/uFhYWqqSkRC6XS7/85S/1b//2b/qf//kf7d69W+PHj1dCQoLzSaUePXpo+PDhmjRpkrZv36433nhDU6ZM0W233aaEhARJ0tixY+V2uzVx4kQVFRVp1apVeuKJJ5SZmVlvjQMAAHu1rOsBb7/9toYMGeLsnwkVEyZM0IoVK/SrX/1KJ0+e1OTJk3X8+HHdcMMN2rBhg8LDw51jVq5cqSlTpmjo0KEKCwvT6NGj9eSTTzrzUVFR2rhxozIyMjRgwADFxMRo1qxZQd8VAwAALl7f63tgmjO+Bwb47vgemMbB98AA59ck3wMDAADQGAgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxT7wGmc+fOcrlcNbaMjAxJ0uDBg2vM3XvvvUHnKCkpUXp6uiIjIxUbG6vp06erqqqqvpcKAAAs1bK+T/jWW2/p9OnTzv6ePXv04x//WD/96U+dsUmTJmnevHnOfmRkpPPz6dOnlZ6eLp/Pp61bt6q0tFTjx49Xq1at9Oijj9b3cgEAgIXqPcB06NAhaH/+/Pnq2rWrbrzxRmcsMjJSPp+v1uM3btyovXv3Kjc3V3FxcerXr58eeeQRzZgxQ3PmzJHb7a7vJQMAAMs06DMwp06d0p///GfdfffdcrlczvjKlSsVExOjq666SllZWfryyy+dufz8fPXu3VtxcXHOWGpqqgKBgIqKis55rYqKCgUCgaANAACEpnq/A3O2NWvW6Pjx47rzzjudsbFjx6pTp05KSEjQrl27NGPGDBUXF+ull16SJPn9/qDwIsnZ9/v957xWdna25s6dW/9NAACAZqdBA8zSpUuVlpamhIQEZ2zy5MnOz71791Z8fLyGDh2qAwcOqGvXrhd8raysLGVmZjr7gUBAiYmJF3w+AADQfDVYgPnwww+Vm5vr3Fk5l6SkJEnS/v371bVrV/l8Pm3fvj2opqysTJLO+dyMJHk8Hnk8nu+5agAAYIMGewZm+fLlio2NVXp6+rfWFRYWSpLi4+MlScnJydq9e7eOHDni1OTk5Mjr9apnz54NtVwAAGCRBrkDU11dreXLl2vChAlq2fIflzhw4ICee+45jRgxQu3bt9euXbs0depUDRo0SH369JEkpaSkqGfPnrrjjju0YMEC+f1+zZw5UxkZGdxhAQAAkhoowOTm5qqkpER333130Ljb7VZubq4ef/xxnTx5UomJiRo9erRmzpzp1LRo0UJr167Vfffdp+TkZLVu3VoTJkwI+t4YAABwcWuQAJOSkiJjTI3xxMREvfbaa+c9vlOnTlq/fn1DLA0AAIQA/hYSAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHXqPcDMmTNHLpcraOvevbsz//XXXysjI0Pt27fXJZdcotGjR6usrCzoHCUlJUpPT1dkZKRiY2M1ffp0VVVV1fdSAQCApVo2xEl79eql3Nzcf1yk5T8uM3XqVK1bt06rV69WVFSUpkyZoltuuUVvvPGGJOn06dNKT0+Xz+fT1q1bVVpaqvHjx6tVq1Z69NFHG2K5AADAMg0SYFq2bCmfz1djvLy8XEuXLtVzzz2nH/3oR5Kk5cuXq0ePHnrzzTd13XXXaePGjdq7d69yc3MVFxenfv366ZFHHtGMGTM0Z84cud3uhlgyAACwSIM8A7Nv3z4lJCTo8ssv17hx41RSUiJJKigoUGVlpYYNG+bUdu/eXR07dlR+fr4kKT8/X71791ZcXJxTk5qaqkAgoKKiooZYLgAAsEy934FJSkrSihUr1K1bN5WWlmru3LkaOHCg9uzZI7/fL7fbrejo6KBj4uLi5Pf7JUl+vz8ovJyZPzN3LhUVFaqoqHD2A4FAPXUEAACam3oPMGlpac7Pffr0UVJSkjp16qS//OUvioiIqO/LObKzszV37twGOz8AAGg+Gvxj1NHR0bryyiu1f/9++Xw+nTp1SsePHw+qKSsrc56Z8fl8NT6VdGa/tudqzsjKylJ5ebmzHT58uH4bAQAAzUaDB5gTJ07owIEDio+P14ABA9SqVStt2rTJmS8uLlZJSYmSk5MlScnJydq9e7eOHDni1OTk5Mjr9apnz57nvI7H45HX6w3aAABAaKr3t5AeeughjRw5Up06ddInn3yi2bNnq0WLFhozZoyioqI0ceJEZWZmql27dvJ6vXrggQeUnJys6667TpKUkpKinj176o477tCCBQvk9/s1c+ZMZWRkyOPx1PdyAQCAheo9wHz00UcaM2aMPv/8c3Xo0EE33HCD3nzzTXXo0EGS9NhjjyksLEyjR49WRUWFUlNT9dRTTznHt2jRQmvXrtV9992n5ORktW7dWhMmTNC8efPqe6kAAMBSLmOMaepFNIRAIKCoqCiVl5fzdhJwHp0fXtfUS7goHJqf3tRLAJq97/r7m7+FBAAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDotm3oBAHCx6PzwuqZeQp0dmp/e1EsAasUdGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGCdeg8w2dnZ+sEPfqA2bdooNjZWN998s4qLi4NqBg8eLJfLFbTde++9QTUlJSVKT09XZGSkYmNjNX36dFVVVdX3cgEAgIVa1vcJX3vtNWVkZOgHP/iBqqqq9K//+q9KSUnR3r171bp1a6du0qRJmjdvnrMfGRnp/Hz69Gmlp6fL5/Np69atKi0t1fjx49WqVSs9+uij9b1kAABgmXoPMBs2bAjaX7FihWJjY1VQUKBBgwY545GRkfL5fLWeY+PGjdq7d69yc3MVFxenfv366ZFHHtGMGTM0Z84cud3u+l42AACwSIM/A1NeXi5JateuXdD4ypUrFRMTo6uuukpZWVn68ssvnbn8/Hz17t1bcXFxzlhqaqoCgYCKiopqvU5FRYUCgUDQBgAAQlO934E5W3V1tX75y1/qhz/8oa666ipnfOzYserUqZMSEhK0a9cuzZgxQ8XFxXrppZckSX6/Pyi8SHL2/X5/rdfKzs7W3LlzG6gTAADQnDRogMnIyNCePXv097//PWh88uTJzs+9e/dWfHy8hg4dqgMHDqhr164XdK2srCxlZmY6+4FAQImJiRe2cAAA0Kw12FtIU6ZM0dq1a7VlyxZddtll31qblJQkSdq/f78kyefzqaysLKjmzP65npvxeDzyer1BGwAACE31HmCMMZoyZYr++te/avPmzerSpct5jyksLJQkxcfHS5KSk5O1e/duHTlyxKnJycmR1+tVz54963vJAADAMvX+FlJGRoaee+45vfzyy2rTpo3zzEpUVJQiIiJ04MABPffccxoxYoTat2+vXbt2aerUqRo0aJD69OkjSUpJSVHPnj11xx13aMGCBfL7/Zo5c6YyMjLk8Xjqe8kAgHPo/PC6pl5CnR2an97US0AjqPc7ME8//bTKy8s1ePBgxcfHO9uqVaskSW63W7m5uUpJSVH37t01bdo0jR49Wv/7v//rnKNFixZau3atWrRooeTkZN1+++0aP3580PfGAACAi1e934ExxnzrfGJiol577bXznqdTp05av359fS0LAACEEP4WEgAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGCdlk29ACDUdH54XVMvAQBCHndgAACAdQgwAADAOgQYAABgHZ6BAQCEFJ5DaxyH5qc36fW5AwMAAKzDHRg0a/yXFACgNtyBAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHT6FdJHg0zwAgFDCHRgAAGAdAgwAALAObyFdAN6OAQCgaXEHBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOs06wCxZskSdO3dWeHi4kpKStH379qZeEgAAaAaabYBZtWqVMjMzNXv2bO3YsUN9+/ZVamqqjhw50tRLAwAATazZBphFixZp0qRJuuuuu9SzZ0/97ne/U2RkpJYtW9bUSwMAAE2sWX4T76lTp1RQUKCsrCxnLCwsTMOGDVN+fn6tx1RUVKiiosLZLy8vlyQFAoF6X191xZf1fk4AAGzSEL9fzz6vMeZb65plgPnss890+vRpxcXFBY3HxcXpvffeq/WY7OxszZ07t8Z4YmJig6wRAICLWdTjDXv+L774QlFRUeecb5YB5kJkZWUpMzPT2a+urtbRo0fVvn17uVyuRltHIBBQYmKiDh8+LK/X22jXbQr0GproNTTRa2gKxV6NMfriiy+UkJDwrXXNMsDExMSoRYsWKisrCxovKyuTz+er9RiPxyOPxxM0Fh0d3VBLPC+v1xsy/zCdD72GJnoNTfQamkKt12+783JGs3yI1+12a8CAAdq0aZMzVl1drU2bNik5ObkJVwYAAJqDZnkHRpIyMzM1YcIEXXPNNbr22mv1+OOP6+TJk7rrrruaemkAAKCJNdsA87Of/UyffvqpZs2aJb/fr379+mnDhg01Huxtbjwej2bPnl3j7axQRK+hiV5DE72Gpoup129ymfN9TgkAAKCZaZbPwAAAAHwbAgwAALAOAQYAAFiHAAMAAKxz0QWYzp07y+Vy1dgyMjKcmvz8fP3oRz9S69at5fV6NWjQIH311VeSpEOHDmnixInq0qWLIiIi1LVrV82ePVunTp0Kus6uXbs0cOBAhYeHKzExUQsWLKixltWrV6t79+4KDw9X7969tX79+qB5Y4xmzZql+Ph4RUREaNiwYdq3b1+j9Xq2iooK9evXTy6XS4WFhSHb67p165SUlKSIiAi1bdtWN998c9B8SUmJ0tPTFRkZqdjYWE2fPl1VVVVBNa+++qquvvpqeTweXXHFFVqxYkWN9S5ZskSdO3dWeHi4kpKStH379kbt9f3339eoUaMUExMjr9erG264QVu2bLGq10OHDtU653K5tHr16kbv4+uvv1ZGRobat2+vSy65RKNHj67xZZwN2evOnTs1ZswYJSYmKiIiQj169NATTzxR4zqh0OvZPv/8c1122WVyuVw6fvx4yPa6YsUK9enTR+Hh4YqNjQ36911qHq/Djc5cZI4cOWJKS0udLScnx0gyW7ZsMcYYs3XrVuP1ek12drbZs2ePee+998yqVavM119/bYwx5m9/+5u58847zSuvvGIOHDhgXn75ZRMbG2umTZvmXKO8vNzExcWZcePGmT179pjnn3/eREREmN///vdOzRtvvGFatGhhFixYYPbu3WtmzpxpWrVqZXbv3u3UzJ8/30RFRZk1a9aYnTt3mptuusl06dLFfPXVV43S69kefPBBk5aWZiSZd955JyR7ffHFF03btm3N008/bYqLi01RUZFZtWqVM19VVWWuuuoqM2zYMPPOO++Y9evXm5iYGJOVleXUfPDBByYyMtJkZmaavXv3msWLF5sWLVqYDRs2ODUvvPCCcbvdZtmyZaaoqMhMmjTJREdHm7Kyskbr9Z/+6Z/MiBEjzM6dO837779v7r//fhMZGWlKS0ut6bWqqiporrS01MydO9dccskl5osvvmj0Pu69916TmJhoNm3aZN5++21z3XXXmeuvv/479VkfvS5dutQ8+OCD5tVXXzUHDhwwf/rTn0xERIRZvHhxyPV6tlGjRjmvTceOHQvJXv/zP//TJCQkmJUrV5r9+/ebnTt3mpdfftmZby6vw43togsw3/SLX/zCdO3a1VRXVxtjjElKSjIzZ86s0zkWLFhgunTp4uw/9dRTpm3btqaiosIZmzFjhunWrZuzf+utt5r09PSg8yQlJZl77rnHGGNMdXW18fl8ZuHChc788ePHjcfjMc8//3yd1nfGhfa6fv160717d1NUVFQjwIRKr5WVlebSSy81f/jDH85Zs379ehMWFmb8fr8z9vTTTxuv1+v0/6tf/cr06tUr6Lif/exnJjU11dm/9tprTUZGhrN/+vRpk5CQYLKzs+vW5P9X114//fRTI8nk5eU5Y4FAwEgyOTk5VvX6Tf369TN33323s99YfRw/fty0atXKrF692ql59913jSSTn5/fKL3W5v777zdDhgxx9kOt16eeesrceOONZtOmTTUCTKj0evToURMREWFyc3PPec7m+jrc0C66t5DOdurUKf35z3/W3XffLZfLpSNHjmjbtm2KjY3V9ddfr7i4ON144436+9///q3nKS8vV7t27Zz9/Px8DRo0SG632xlLTU1VcXGxjh075tQMGzYs6DypqanKz8+XJB08eFB+vz+oJioqSklJSU5NY/RaVlamSZMm6U9/+pMiIyNrnDdUet2xY4c+/vhjhYWFqX///oqPj1daWpr27NkT1Gvv3r2DvkwxNTVVgUBARUVF36nXU6dOqaCgIKgmLCxMw4YNa7Re27dvr27duunZZ5/VyZMnVVVVpd///veKjY3VgAEDrOn1mwoKClRYWKiJEyc6Y43VR0FBgSorK4Nqunfvro4dOzZar7Wp7bUpVHrdu3ev5s2bp2effVZhYTV/lYVKrzk5OaqurtbHH3+sHj166LLLLtOtt96qw4cPB/Xa3F6HG8NFHWDWrFmj48eP684775QkffDBB5KkOXPmaNKkSdqwYYOuvvpqDR069JzvA+7fv1+LFy/WPffc44z5/f4a3xh8Zt/v939rzdnzZx9XW01D92qM0Z133ql7771X11xzTa3nDZVez66ZOXOm1q5dq7Zt22rw4ME6evTo9+41EAjoq6++0meffabTp083aa8ul0u5ubl655131KZNG4WHh2vRokXasGGD2rZta02v37R06VL16NFD119/vTPWWH34/X653e4af0C2MXv9pq1bt2rVqlWaPHmyMxYqvVZUVGjMmDFauHChOnbsWOtxodLrBx98oOrqaj366KN6/PHH9eKLL+ro0aP68Y9/7Dx72RxfhxvDRR1gli5dqrS0NOdPdldXV0uS7rnnHt11113q37+/HnvsMXXr1k3Lli2rcfzHH3+s4cOH66c//akmTZrUqGuvqwvpdfHixfriiy+UlZXVZOu+EBfS65maX//61xo9erQGDBig5cuXn/PBwebiQno1xigjI0OxsbF6/fXXtX37dt18880aOXKkSktLm6yX8/lmr2f76quv9Nxzz533joQtvm+ve/bs0ahRozR79mylpKQ05FK/twvpNSsrSz169NDtt9/eWMusFxfSa3V1tSorK/Xkk08qNTVV1113nZ5//nnt27evxoP3F5uLNsB8+OGHys3N1c9//nNnLD4+XpLUs2fPoNoePXqopKQkaOyTTz7RkCFDdP311+uZZ54JmvP5fDWeUj+z7/P5vrXm7Pmzj6ut5ru60F43b96s/Px8eTwetWzZUldccYUk6ZprrtGECRNCqtfaajwejy6//HKn5vv06vV6FRERoZiYGLVo0aLJ/39du3atXnjhBf3whz/U1VdfraeeekoRERH64x//aE2vZ3vxxRf15Zdfavz48UHjjdWHz+fTqVOnanwKpjF7PWPv3r0aOnSoJk+erJkzZwbNhUqvmzdv1urVq9WyZUu1bNlSQ4cOlSTFxMRo9uzZIdVrbf9Od+jQQTExMfXy2tQQr8ON5aINMMuXL1dsbKzS09Odsc6dOyshIUHFxcVBte+//746derk7H/88ccaPHiw81/p33z/NTk5WXl5eaqsrHTGcnJy1K1bN+cWfXJysjZt2hR0XE5OjpKTkyVJXbp0kc/nC6oJBALatm2bU9PQvT755JPauXOnCgsLVVhY6HzkbtWqVfr3f//3kOp1wIAB8ng8QTWVlZU6dOiQU5OcnKzdu3fryJEjQX14vV7nxeV8vbrdbg0YMCCoprq6Wps2bWq0Xr/88ktJqvHPbVhYmHMHx4Zez7Z06VLddNNN6tChQ9B4Y/UxYMAAtWrVKqimuLhYJSUljdarJBUVFWnIkCGaMGGC8+/o2UKl1//+7/8Oem36wx/+IEl6/fXXnY8Xh0qvP/zhD53rnnH06FF99tlnQa9Nzel1uNE09VPETeH06dOmY8eOZsaMGTXmHnvsMeP1es3q1avNvn37zMyZM014eLjZv3+/McaYjz76yFxxxRVm6NCh5qOPPgr6+NsZx48fN3FxceaOO+4we/bsMS+88IKJjIys8ZG2li1bmt/+9rfm3XffNbNnz671I23R0dHm5ZdfNrt27TKjRo2q80favk+v33Tw4MEan0IKpV5/8YtfmEsvvdS88sor5r333jMTJ040sbGx5ujRo8aYf3wkNyUlxRQWFpoNGzaYDh061PqR3OnTp5t3333XLFmypNaPbno8HrNixQqzd+9eM3nyZBMdHR30SZmG7PXTTz817du3N7fccospLCw0xcXF5qGHHjKtWrUyhYWFVvVqjDH79u0zLpfL/O1vf6sx15h93HvvvaZjx45m8+bN5u233zbJyckmOTn5O/f5fXvdvXu36dChg7n99tuDXpeOHDkScr1+05YtW875MepQ6HXUqFGmV69e5o033jC7d+82//zP/2x69uxpTp06ZYxpXq/DjemiDDCvvPKKkWSKi4trnc/OzjaXXXaZiYyMNMnJyeb111935pYvX24k1bqdbefOneaGG24wHo/HXHrppWb+/Pk1rvOXv/zFXHnllcbtdptevXqZdevWBc1XV1eb3/zmNyYuLs54PB4zdOjQc665IXr9ptoCTCj1eurUKTNt2jQTGxtr2rRpY4YNG2b27NkTVHPo0CGTlpZmIiIiTExMjJk2bZqprKwMqtmyZYvp16+fcbvd5vLLLzfLly+vsZbFixebjh07Grfbba699lrz5ptvNmqvb731lklJSTHt2rUzbdq0Mdddd51Zv369lb1mZWWZxMREc/r06VrnG6uPr776ytx///2mbdu2JjIy0vzkJz8J+g+bhu519uzZtb4uderUKeR6/abaAsyZ8VDotby83Nx9990mOjratGvXzvzkJz8xJSUlQTXN5XW4MbmMMaYRb/gAAAB8bxftMzAAAMBeBBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWOf/AQ8gNDIcr4taAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"oops, error in HDvPop, let's fix\")\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "1ee72cf0-618e-411f-aa37-518ac2c679ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "#optional intermediate save\n",
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"underPatched.csv\" #\"contigUnpatchedB.csv\"  #underPatched\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "31ad110c-5b9f-4a7a-aad4-2a27f475ab34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04  is default maxSD.  Enter updated value\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "e.g. 0.04  0.04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for moving on to next overused unit is 1.03\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 1097 units out of 4023 with usage above 1.02\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this block reduces overuse, with exchanges up to 38468\n",
      "current avg and SD of unit usage are 1.00063 0.05842 . Now trying to reduce up to 1097 units' overusage\n",
      "starting to reduce usage for unit 3845 with usage 1.24263 . Total units tried = 1\n",
      "try to drop unit 3845 from 46 th HD.  usage down to 1.181918899584787\n",
      "try to drop unit 3845 from 92 th HD.  usage down to 1.109192607596579\n",
      "all done trying to reduce usage of unit 3845 final usage = 1.0083 94 sec elapsed 113 0 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 1.0006 0.05688\n",
      "starting to reduce usage for unit 3368 with usage 1.20664 . Total units tried = 2\n",
      "try to drop unit 3368 from 27 th HD.  usage down to 1.2042035362643937\n",
      "try to drop unit 3368 from 54 th HD.  usage down to 1.1990161355651474\n",
      "try to drop unit 3368 from 81 th HD.  usage down to 1.1990161355651474\n",
      "try to drop unit 3368 from 108 th HD.  usage down to 1.190902942088652\n",
      "try to drop unit 3368 from 135 th HD.  usage down to 1.1856765481493712\n",
      "all done trying to reduce usage of unit 3368 final usage = 1.18541 116 sec elapsed 19 0 successful, failed patches, couldn't start= 117\n",
      "current avg and SD of usage are 1.0006 0.0565\n",
      "starting to reduce usage for unit 658 with usage 1.20235 . Total units tried = 3\n",
      "try to drop unit 658 from 49 th HD.  usage down to 1.1494479288334511\n",
      "try to drop unit 658 from 98 th HD.  usage down to 1.0627646564622553\n",
      "try to drop unit 658 from 147 th HD.  usage down to 1.021995924231597\n",
      "all done trying to reduce usage of unit 658 final usage = 1.01879 141 sec elapsed 81 2 successful, failed patches, couldn't start= 66\n",
      "current avg and SD of usage are 1.0006 0.05624\n",
      "starting to reduce usage for unit 3361 with usage 1.19697 . Total units tried = 4\n",
      "try to drop unit 3361 from 15 th HD.  usage down to 1.1782587341201598\n",
      "try to drop unit 3361 from 30 th HD.  usage down to 1.1617528956135887\n",
      "try to drop unit 3361 from 45 th HD.  usage down to 1.1389587472641334\n",
      "try to drop unit 3361 from 60 th HD.  usage down to 1.1288114064325094\n",
      "try to drop unit 3361 from 75 th HD.  usage down to 1.1143202186610508\n",
      "all done trying to reduce usage of unit 3361 final usage = 1.11358 183 sec elapsed 54 15 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00059 0.05505\n",
      "starting to reduce usage for unit 199 with usage 1.1704 . Total units tried = 5\n",
      "try to drop unit 199 from 31 th HD.  usage down to 1.1097281147597118\n",
      "try to drop unit 199 from 62 th HD.  usage down to 1.0202165327113506\n",
      "all done trying to reduce usage of unit 199 final usage = 1.01648 189 sec elapsed 55 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00058 0.05153\n",
      "starting to reduce usage for unit 5 with usage 1.15195 . Total units tried = 6\n",
      "try to drop unit 5 from 47 th HD.  usage down to 1.099297423050508\n",
      "try to drop unit 5 from 94 th HD.  usage down to 1.0757637029150646\n",
      "try to drop unit 5 from 141 th HD.  usage down to 1.065603364336762\n",
      "try to drop unit 5 from 188 th HD.  usage down to 1.0348078031322392\n",
      "all done trying to reduce usage of unit 5 final usage = 1.01814 197 sec elapsed 62 1 successful, failed patches, couldn't start= 134\n",
      "current avg and SD of usage are 1.00055 0.04944\n",
      "starting to reduce usage for unit 484 with usage 1.15227 . Total units tried = 7\n"
     ]
    },
    {
     "ename": "Exception",
     "evalue": "('ERROR! Attempted to shed list', {282}, 'but it was never connected to the rest of the unit list')",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[122], line 100\u001b[0m\n\u001b[0;32m     98\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m     99\u001b[0m \u001b[38;5;66;03m#contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\u001b[39;00m\n\u001b[1;32m--> 100\u001b[0m contig \u001b[38;5;241m=\u001b[39m \u001b[43mavoidedIsland\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrySet\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munitNbrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mset\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mshedU\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    101\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m contig: \u001b[38;5;66;03m#and cContig:\u001b[39;00m\n\u001b[0;32m    102\u001b[0m     notYetPicked \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "Cell \u001b[1;32mIn[2], line 786\u001b[0m, in \u001b[0;36mavoidedIsland\u001b[1;34m(UNITLIST, UNITNBRS, SHEDLIST, MAXLOOPS)\u001b[0m\n\u001b[0;32m    784\u001b[0m nbrsOfSHEDLIST \u001b[38;5;241m=\u001b[39m nbrsOfSHEDLIST\u001b[38;5;241m.\u001b[39mintersection(shrunkSet)\n\u001b[0;32m    785\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(nbrsOfSHEDLIST) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 786\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mERROR! Attempted to shed list\u001b[39m\u001b[38;5;124m\"\u001b[39m,SHEDLIST,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut it was never connected to the rest of the unit list\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    787\u001b[0m firstNo \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(nbrsOfSHEDLIST))  \u001b[38;5;66;03m#pick one of these still-in-UL neighbors as a starter   \u001b[39;00m\n\u001b[0;32m    788\u001b[0m firstGroup \u001b[38;5;241m=\u001b[39m getContigFromStarter(firstNo, nbrsOfSHEDLIST, UNITNBRS) \u001b[38;5;66;03m#first find all neighbors that directly connect\u001b[39;00m\n",
      "\u001b[1;31mException\u001b[0m: ('ERROR! Attempted to shed list', {282}, 'but it was never connected to the rest of the unit list')"
     ]
    }
   ],
   "source": [
    "#SECOND PATCHING BLOCK -- OVERUSERS.  using 5/24/24 new avoidedIsland method  #corrected 5/26 for MO OUUc-intrsxn\n",
    "maxSD = 0.04 #0.06  #0.08  #0.04\n",
    "print(maxSD,\" is default maxSD.  Enter updated value\")\n",
    "maxSD = float(input(\"e.g. 0.04 \"))\n",
    "stopMaxUse = 1.03  #1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for moving on to next overused unit is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "nBigUsers = 5\n",
    "maxExchangePop = 0.05*aDP\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP \n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "#maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the districts a unit is drawn into\n",
    "#maxNtries = 10 #overrirde\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #each round, find the (5) most overused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nBigFound, bigUsers = 0,0,list()\n",
    "    while nBigFound < nBigUsers:\n",
    "        consideredBigU = idx[-idxNo-1]\n",
    "        if consideredBigU not in attemptedBigUs:\n",
    "            bigUsers.append(consideredBigU)\n",
    "            nBigFound +=1\n",
    "        idxNo +=1\n",
    "    OUUclusters = [ [b] + unitNbrs[b] for b in bigUsers ]\n",
    "    bigOverUse = [np.sum([(unitUse[j] - 1.) for j in OUUclusters[i] ]) for i in range(nBigUsers) ]\n",
    "    bigI = bigOverUse.index(np.max(bigOverUse))\n",
    "    OUU = bigUsers[ bigI ]  #pick the unit that centers cluster with most overuse\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    OUUc = OUUclusters[bigI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUU2nbrSet = set( unitNbrs[OUU])\n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.union(set(unitNbrs[u])) \n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.difference({u})\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUU2nbrSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OUUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists) #work from farthest HD in\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.5*len(ouuHDs): #arbitrarily only examine 50% of HDs, biasing farthest ones\n",
    "        idxNo -=1\n",
    "        if idxNo % int(0.1*len(ouuHDs)) == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD.  usage down to\",unitUse[OUU] )\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).difference(set(OUUc))\n",
    "        contig = avoidedIsland(HDunitList[t], unitNbrs, list( set(OUUc).intersection(set(HDunitList[t])) ) )\n",
    "        #contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not contig:\n",
    "        #if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                #trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                #contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "                contig = avoidedIsland(HDunitList[t], unitNbrs, [OUU])\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(OUUc))\n",
    "        if contig: # and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    #contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    contig = avoidedIsland(list(trySet), unitNbrs, [shedU])\n",
    "                    if contig: #and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative)  #NEW FOR GA 3/11/24\n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...  \n",
    "                    #canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    canAdd = avoidedEnclave(list(trySet), unitNbrs, [unitNoToAdd])\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= 2.* maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "6d17f3e1-42da-4a38-b80b-b01b1860462e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2490\n"
     ]
    }
   ],
   "source": [
    "print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "366a57c9-d9dd-40b5-b5a8-9fad44f434f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resetting this HD to its unpatched to avoid the raise Exception error\n"
     ]
    }
   ],
   "source": [
    "HDunitList[t] = unpatchedHDlist[t].copy()\n",
    "print(\"resetting this HD to its unpatched to avoid the raise Exception error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "b6ce6a26-db87-4a62-bb75-08a110cd5776",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "oops, needed to fix minor error in OUUc - intersxn HDul.  Continuing here\n",
      "0.04  is default maxSD.  Enter updated value\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "e.g. 0.04  0.04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for moving on to next overused unit is 1.03\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 1032 units out of 4023 with usage above 1.02\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this block reduces overuse, with exchanges up to 38468\n",
      "current avg and SD of unit usage are 1.00055 0.04935 . Now trying to reduce up to 1032 units' overusage\n",
      "starting to reduce usage for unit 484 with usage 1.14767 . Total units tried = 1\n",
      "try to drop unit 484 from 28 th HD.  usage down to 1.1114217211518829\n",
      "try to drop unit 484 from 56 th HD.  usage down to 1.0401849709329924\n",
      "all done trying to reduce usage of unit 484 final usage = 1.0194 9 sec elapsed 59 1 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00054 0.0471\n",
      "starting to reduce usage for unit 3826 with usage 1.21187 . Total units tried = 2\n",
      "try to drop unit 3826 from 38 th HD.  usage down to 1.1653688687395003\n",
      "try to drop unit 3826 from 76 th HD.  usage down to 1.1109421042994478\n",
      "try to drop unit 3826 from 114 th HD.  usage down to 1.0361985620267953\n",
      "all done trying to reduce usage of unit 3826 final usage = 1.01907 146 sec elapsed 102 1 successful, failed patches, couldn't start= 37\n",
      "current avg and SD of usage are 1.00053 0.04535\n",
      "starting to reduce usage for unit 279 with usage 1.12886 . Total units tried = 3\n",
      "try to drop unit 279 from 46 th HD.  usage down to 1.03416051534767\n",
      "all done trying to reduce usage of unit 279 final usage = 1.01872 160 sec elapsed 49 3 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00052 0.04367\n",
      "starting to reduce usage for unit 3765 with usage 1.12329 . Total units tried = 4\n",
      "try to drop unit 3765 from 36 th HD.  usage down to 1.106788024461054\n",
      "try to drop unit 3765 from 72 th HD.  usage down to 1.07963963097442\n",
      "try to drop unit 3765 from 108 th HD.  usage down to 1.0591720792803512\n",
      "try to drop unit 3765 from 144 th HD.  usage down to 1.0397170520525105\n",
      "try to drop unit 3765 from 180 th HD.  usage down to 1.020651957225015\n",
      "all done trying to reduce usage of unit 3765 final usage = 1.01977 314 sec elapsed 118 32 successful, failed patches, couldn't start= 31\n",
      "current avg and SD of usage are 1.00051 0.04237\n",
      "starting to reduce usage for unit 124 with usage 1.1207 . Total units tried = 5\n",
      "try to drop unit 124 from 27 th HD.  usage down to 1.109464260502129\n",
      "try to drop unit 124 from 54 th HD.  usage down to 1.0803675047884398\n",
      "try to drop unit 124 from 81 th HD.  usage down to 1.0631974814266159\n",
      "try to drop unit 124 from 108 th HD.  usage down to 1.0325994859716046\n",
      "all done trying to reduce usage of unit 124 final usage = 1.01903 323 sec elapsed 52 2 successful, failed patches, couldn't start= 69\n",
      "current avg and SD of usage are 1.0005 0.04039\n",
      "starting to reduce usage for unit 378 with usage 1.10003 . Total units tried = 6\n",
      "try to drop unit 378 from 21 th HD.  usage down to 1.063600411573663\n",
      "try to drop unit 378 from 42 th HD.  usage down to 1.0363142419722402\n",
      "all done trying to reduce usage of unit 378 final usage = 1.01905 332 sec elapsed 43 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00049 0.03917\n"
     ]
    }
   ],
   "source": [
    "print(\"oops, needed to fix minor error in OUUc - intersxn HDul.  Continuing here\")\n",
    "#SECOND PATCHING BLOCK -- OVERUSERS.  using 5/24/24 new avoidedIsland method\n",
    "maxSD = 0.04 #0.06  #0.08  #0.04\n",
    "print(maxSD,\" is default maxSD.  Enter updated value\")\n",
    "maxSD = float(input(\"e.g. 0.04 \"))\n",
    "stopMaxUse = 1.03  #1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for moving on to next overused unit is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "nBigUsers = 5\n",
    "maxExchangePop = 0.05*aDP\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP \n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "#maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the districts a unit is drawn into\n",
    "#maxNtries = 10 #overrirde\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #each round, find the (5) most overused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nBigFound, bigUsers = 0,0,list()\n",
    "    while nBigFound < nBigUsers:\n",
    "        consideredBigU = idx[-idxNo-1]\n",
    "        if consideredBigU not in attemptedBigUs:\n",
    "            bigUsers.append(consideredBigU)\n",
    "            nBigFound +=1\n",
    "        idxNo +=1\n",
    "    OUUclusters = [ [b] + unitNbrs[b] for b in bigUsers ]\n",
    "    bigOverUse = [np.sum([(unitUse[j] - 1.) for j in OUUclusters[i] ]) for i in range(nBigUsers) ]\n",
    "    bigI = bigOverUse.index(np.max(bigOverUse))\n",
    "    OUU = bigUsers[ bigI ]  #pick the unit that centers cluster with most overuse\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    OUUc = OUUclusters[bigI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUU2nbrSet = set( unitNbrs[OUU])\n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.union(set(unitNbrs[u])) \n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.difference({u})\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUU2nbrSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OUUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists) #work from farthest HD in\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.5*len(ouuHDs): #arbitrarily only examine 50% of HDs, biasing farthest ones\n",
    "        idxNo -=1\n",
    "        if idxNo % int(0.1*len(ouuHDs)) == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD.  usage down to\",unitUse[OUU] )\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).difference(set(OUUc))\n",
    "        contig = avoidedIsland(HDunitList[t], unitNbrs, list( set(OUUc).intersection(set(HDunitList[t])) ) )\n",
    "        #contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if contig:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(OUUc))\n",
    "        else: #dropping the cluster creates a problem (likely an HD discontig).  Try just dropping the primary OUU\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                #trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                #contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "                contig = avoidedIsland(HDunitList[t], unitNbrs, set([OUU]))\n",
    "            \n",
    "        if contig: # and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    #contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    contig = avoidedIsland(list(trySet), unitNbrs, [shedU] )\n",
    "                    if contig: #and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative)  #NEW FOR GA 3/11/24\n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...  \n",
    "                    #canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    canAdd = avoidedEnclave(list(trySet), unitNbrs, [unitNoToAdd])\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= 2.* maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92f90621-e7fc-4891-a5cd-69033c3c48e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#copy one of two above blocks and run again if needed (n/a for NC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "cb60da52-ca13-45dd-8bf0-475213183a0d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "underused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"overused units\")\n",
    "maxPlot = 1.06\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > maxPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"underused units\")\n",
    "minPlot = 0.94\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa912158-e8e9-46f3-8c3d-f6b17c26e62c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note - for MA, above is second run-through after overuse, then underuse pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "0d4f1372-6044-4803-91d9-de4b95a40083",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.0007 1.00049 and their SDs are 0.10046 0.03917\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 4604\n",
      "uh-oh, HD 165 has contiguity, complement-contiguity of False True\n",
      "uh-oh, HD 180 has contiguity, complement-contiguity of False True\n",
      "working on HD 300 out of 4604\n",
      "working on HD 600 out of 4604\n",
      "uh-oh, HD 734 has contiguity, complement-contiguity of True False\n",
      "working on HD 900 out of 4604\n",
      "uh-oh, HD 993 has contiguity, complement-contiguity of False True\n",
      "uh-oh, HD 996 has contiguity, complement-contiguity of False True\n",
      "uh-oh, HD 997 has contiguity, complement-contiguity of False True\n",
      "working on HD 1200 out of 4604\n",
      "working on HD 1500 out of 4604\n",
      "uh-oh, HD 1657 has contiguity, complement-contiguity of True False\n",
      "uh-oh, HD 1772 has contiguity, complement-contiguity of False True\n",
      "working on HD 1800 out of 4604\n",
      "working on HD 2100 out of 4604\n",
      "uh-oh, HD 2334 has contiguity, complement-contiguity of True False\n",
      "uh-oh, HD 2398 has contiguity, complement-contiguity of False True\n",
      "working on HD 2400 out of 4604\n",
      "uh-oh, HD 2400 has contiguity, complement-contiguity of False True\n",
      "uh-oh, HD 2416 has contiguity, complement-contiguity of False True\n",
      "uh-oh, HD 2418 has contiguity, complement-contiguity of False True\n",
      "uh-oh, HD 2426 has contiguity, complement-contiguity of False True\n",
      "uh-oh, HD 2490 has contiguity, complement-contiguity of False True\n",
      "uh-oh, HD 2500 has contiguity, complement-contiguity of True False\n",
      "uh-oh, HD 2537 has contiguity, complement-contiguity of True False\n",
      "uh-oh, HD 2554 has contiguity, complement-contiguity of False True\n",
      "uh-oh, HD 2644 has contiguity, complement-contiguity of False True\n",
      "uh-oh, HD 2657 has contiguity, complement-contiguity of False True\n",
      "working on HD 2700 out of 4604\n",
      "uh-oh, HD 2820 has contiguity, complement-contiguity of True False\n",
      "uh-oh, HD 2823 has contiguity, complement-contiguity of False True\n",
      "uh-oh, HD 2825 has contiguity, complement-contiguity of True False\n",
      "working on HD 3000 out of 4604\n",
      "working on HD 3300 out of 4604\n",
      "working on HD 3600 out of 4604\n",
      "working on HD 3900 out of 4604\n",
      "working on HD 4200 out of 4604\n",
      "working on HD 4500 out of 4604\n",
      "all done checking HD and complement contiguity for all 4604 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "105ee28c-e042-4d8f-b825-4d55afaf6b7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Lets write these UNIT lists to a file, even though they need fixing\")   #DONE\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"fullyPatchedneedsContig.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "ac9cd133-94f0-494b-ac74-0c584482d97e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "5bd2629e-a44d-45a8-96d5-fdd063817b23",
   "metadata": {},
   "outputs": [],
   "source": [
    "#5-27 -- FIX THESE !!!!!!!!!!!!  MO special triage\n",
    "enclaveTrouble = [734,1657, 2334, 2500, 2537, 2820, 2825]\n",
    "contigTrouble = [993, 996,997,1772,2398,2400,2416,2418,2426,2490,2554,2644, 2657, 2823]\n",
    "#oops, should have included 165 and 180 in above contigTrouble-- missed them\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "dafb5cbd-43ec-4f10-b2f5-1e1ed772e34c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t,fullpop, contig pop/aDP 993 0.99993 0.9993\n",
      "t,fullpop, contig pop/aDP 996 1.00094 0.98877\n",
      "t,fullpop, contig pop/aDP 997 0.99925 0.98645\n",
      "t,fullpop, contig pop/aDP 1772 1.00029 0.99815\n",
      "t,fullpop, contig pop/aDP 2398 1.00061 0.99998\n",
      "t,fullpop, contig pop/aDP 2400 1.00114 1.00052\n",
      "t,fullpop, contig pop/aDP 2416 0.99931 0.99869\n",
      "t,fullpop, contig pop/aDP 2418 1.00026 0.99963\n",
      "t,fullpop, contig pop/aDP 2426 0.99972 0.98998\n",
      "t,fullpop, contig pop/aDP 2490 0.99906 0.99843\n",
      "t,fullpop, contig pop/aDP 2554 0.99886 0.99596\n",
      "t,fullpop, contig pop/aDP 2644 1.00111 0.99228\n",
      "t,fullpop, contig pop/aDP 2657 1.00016 0.99953\n",
      "t,fullpop, contig pop/aDP 2823 1.00057 0.97011\n",
      "     above was discontig.  below are the enclavy\n",
      "t,totalEP, hdPop/aDP, hdPop + enclavePop / aDP 734 2536 1.00109 1.00438\n",
      "t,totalEP, hdPop/aDP, hdPop + enclavePop / aDP 1657 326 1.00095 1.00137\n",
      "t,totalEP, hdPop/aDP, hdPop + enclavePop / aDP 2334 15736 1.00076 1.02121\n",
      "t,totalEP, hdPop/aDP, hdPop + enclavePop / aDP 2500 9253 0.99962 1.01165\n",
      "t,totalEP, hdPop/aDP, hdPop + enclavePop / aDP 2537 326 0.99933 0.99975\n",
      "t,totalEP, hdPop/aDP, hdPop + enclavePop / aDP 2820 9417 0.99889 1.01113\n",
      "t,totalEP, hdPop/aDP, hdPop + enclavePop / aDP 2825 1724 0.99894 1.00118\n"
     ]
    }
   ],
   "source": [
    "#MO special triage\n",
    "for t in contigTrouble:\n",
    "    contigUs = getContigFromStarter(HDunitList[t][0],HDunitList[t], unitNbrs )\n",
    "    contigPop = np.sum([unitPop[u] for u in contigUs])\n",
    "    print(\"t,fullpop, contig pop/aDP\",t,r5(HDvPop[t]/aDP), r5(contigPop/aDP) )\n",
    "print(\"     above was discontig.  below are the enclavy\")\n",
    "for t in enclaveTrouble:\n",
    "    enclaveLists = getEnclaveLists(HDunitList[t],unitNbrs)\n",
    "    nEnclaves = len(enclaveLists)\n",
    "    tEP = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] )\n",
    "    print(\"t,totalEP, hdPop/aDP, hdPop + enclavePop / aDP\",t,int(tEP),r5(HDvPop[t]/aDP), r5((HDvPop[t] + tEP)/aDP) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "0b89d6b2-d236-4ef3-9f72-aa861d7173c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we have 7 HDs with enclaves and 14 discontiguous HDs\n",
      "fixing where we can, the we will re-run squaring up / down where needed\n",
      "t,orig pop/aDP, contig pop/aDP 993 0.9993 0.9993\n",
      "t,orig pop/aDP, contig pop/aDP 996 0.98877 0.98877\n",
      "   we will shore up HD 996 in a below block\n",
      "t,orig pop/aDP, contig pop/aDP 997 0.98645 0.98645\n",
      "   we will shore up HD 997 in a below block\n",
      "t,orig pop/aDP, contig pop/aDP 1772 0.99815 0.99815\n",
      "t,orig pop/aDP, contig pop/aDP 2398 0.99998 0.99998\n",
      "t,orig pop/aDP, contig pop/aDP 2400 1.00052 1.00052\n",
      "t,orig pop/aDP, contig pop/aDP 2416 0.99869 0.99869\n",
      "t,orig pop/aDP, contig pop/aDP 2418 0.99963 0.99963\n",
      "t,orig pop/aDP, contig pop/aDP 2426 0.98998 0.98998\n",
      "   we will shore up HD 2426 in a below block\n",
      "t,orig pop/aDP, contig pop/aDP 2490 0.99843 0.99843\n",
      "t,orig pop/aDP, contig pop/aDP 2554 0.99596 0.99596\n",
      "t,orig pop/aDP, contig pop/aDP 2644 0.99228 0.99228\n",
      "t,orig pop/aDP, contig pop/aDP 2657 0.99953 0.99953\n",
      "t,orig pop/aDP, contig pop/aDP 2823 0.97011 0.97011\n",
      "   we will shore up HD 2823 in a below block\n",
      "t,totalEP, orig hdPop/aDP, hdPop + enclavePop / aDP 734 2536 1.00438 1.00768\n",
      "t,totalEP, orig hdPop/aDP, hdPop + enclavePop / aDP 1657 326 1.00137 1.0018\n",
      "t,totalEP, orig hdPop/aDP, hdPop + enclavePop / aDP 2334 15736 1.02121 1.04167\n",
      "   we will trim down HD 2334 in a below block\n",
      "t,totalEP, orig hdPop/aDP, hdPop + enclavePop / aDP 2500 9253 1.01165 1.02368\n",
      "   we will trim down HD 2500 in a below block\n",
      "t,totalEP, orig hdPop/aDP, hdPop + enclavePop / aDP 2537 326 0.99975 1.00018\n",
      "t,totalEP, orig hdPop/aDP, hdPop + enclavePop / aDP 2820 9417 1.01113 1.02337\n",
      "   we will trim down HD 2820 in a below block\n",
      "t,totalEP, orig hdPop/aDP, hdPop + enclavePop / aDP 2825 1724 1.00118 1.00342\n"
     ]
    }
   ],
   "source": [
    "#more MO special triage\n",
    "print(\"we have\",len(enclaveTrouble),\"HDs with enclaves and\",len(contigTrouble),\"discontiguous HDs\")\n",
    "print(\"fixing where we can, the we will re-run squaring up / down where needed\")\n",
    "underPoppedList, overPoppedList = list(), list()\n",
    "minDistrictPop, maxDistrictPop = 0.99 * aDP, 1.01 * aDP\n",
    "for t in contigTrouble:\n",
    "    contigUs = getContigFromStarter(HDunitList[t][0],HDunitList[t], unitNbrs )\n",
    "    contigPop = np.sum([unitPop[u] for u in contigUs])\n",
    "    HDvPop[t] = contigPop\n",
    "    for u in set(HDunitList[t]).difference(set(contigUs)):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts\n",
    "    HDunitList[t] = contigUs.copy()\n",
    "    print(\"t,orig pop/aDP, contig pop/aDP\",t,r5(HDvPop[t]/aDP), r5(contigPop/aDP) )\n",
    "    if HDvPop[t] < minDistrictPop:\n",
    "        underPoppedList.append(t)\n",
    "        print(\"   we will shore up HD\",t,\"in a below block\")\n",
    "\n",
    "for t in enclaveTrouble:\n",
    "    enclaveLists = getEnclaveLists(HDunitList[t],unitNbrs)\n",
    "    tEP = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] )\n",
    "    HDvPop[t] += tEP\n",
    "    for eL in enclaveLists:\n",
    "        HDunitList[t] += eL\n",
    "        for u in eL:\n",
    "            unitUse[u] += HDweight[t] * nDistricts            \n",
    "    \n",
    "    print(\"t,totalEP, orig hdPop/aDP, hdPop + enclavePop / aDP\",t,int(tEP),r5(HDvPop[t]/aDP), r5((HDvPop[t] + tEP)/aDP) )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        overPoppedList.append(t)\n",
    "        print(\"   we will trim down HD\",t,\"in a below block\")\n",
    "    \n",
    "    #for eL in enclaveLists:\n",
    "    #    for u in eL:\n",
    "    #        plotPoly(unitGeom[u])\n",
    "    #        plotCenter(\"e\",unitCP[u])\n",
    "    #for u in HDunitList[t]:\n",
    "    #    plotPoly(unitGeom[u],0.1)\n",
    "    #HDvPop[t] = np.sum([unitPop[u] for u in HDunitList[t] ] )\n",
    "    #print(\"t,nEnclaves,totalEP, hdPop, saved hdPop\",t,nEnclaves,r3(tEP), r3(hdPop), r3(HDvPop[t]) )\n",
    "    #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "a315a6f5-8659-4bce-8193-b6037647e55f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "re-squaring up these new-found underpopped\n",
      "We will now square up the 4 HDs with pop < 761670 to within 882\n",
      "working on squaring up HD 996 time is now 0\n",
      "We have addressed underpop in a total of 4 HDs\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Bi9dt9/2Jl4ykBC26Ygx32gHfAj7aA4DTRHltg362+t12fyevsblVP1v9rsprG3ppZMDpiyAFAKeBtphp8brtsg76jrYtXrddbbGOKgC4IkgBwGlg8+797d6JOpZJamhu1ebd+3tuUMB3AEEKAE4D+w50HqJc6gAcH4IUAJwGUs9I+PqibtQBOD4EKQA4DeQOG6iMpIR2f2T4qIC+unsvd9jAnhwWcNojSAHAaSA+LqBFV4yRpHZh6uj6oivG8Dwp4BtGkAKA08S0sRl6/McTlZ7k//guPSlBj/94Is+RAr4FPJATAE4j08Zm6Adj0nmyOdBDuvWOVE5OjgKBQLulqKjIq6mqqtLll1+u/v37KxwOa/LkyWppafH69+/fr5tuuknhcFjJycmaOXOmPv/8c99xtm7dqu9///tKSEhQVlaWli5d2m4sZWVlGjVqlBISEjRu3DitX7/e129meuCBB5SRkaF+/fopPz9fO3fu7M7pAsApKT4uoMjwQbpq/JmKDB9EiAK+Rd0KUlu2bFFDQ4O3VFRUSJKuv/56SV+FqGnTpmnq1KnavHmztmzZop///OeKi/vHYW666Sa9//77qqio0O9//3u98cYbmjVrltcfjUY1depUDR06VNXV1Vq2bJkefPBB/frXv/ZqNm7cqBtvvFEzZ87Un//8Z1199dW6+uqrVVtb69UsXbpUv/zlL/XEE09o06ZN6t+/vwoKCtTayq2/AADgG2InYO7cuTZ8+HCLxWJmZpaXl2cLFy7stH779u0mybZs2eK1vfzyyxYIBOxvf/ubmZk99thjlpKSYgcPHvRqFixYYCNHjvTWb7jhBissLPTtOy8vz2bPnm1mZrFYzNLT023ZsmVef1NTk4VCIXv22WeP+/yam5tNkjU3Nx/3NgAAoHf15PXb+cvmhw4d0urVqzVjxgwFAgHt27dPmzZtUmpqqi666CKlpaXpkksu0VtvveVtU1VVpeTkZF1wwQVeW35+vuLi4rRp0yavZvLkyQoGg15NQUGB6urq9Nlnn3k1+fn5vvEUFBSoqqpKkrR79241Njb6apKSkpSXl+fVdOTgwYOKRqO+BQAAoDPOQWrt2rVqamrSLbfcIkn66KOPJEkPPvigbr/9dpWXl2vixImaMmWK992kxsZGpaam+vbTp08fDRw4UI2NjV5NWlqar+bo+tfVHNt/7HYd1XSkpKRESUlJ3pKVlXV8LwYAAPhOcg5SK1eu1PTp05WZmSlJisVikqTZs2fr1ltv1YQJE7R8+XKNHDlSTz311Dcz2m9ZcXGxmpubveWTTz7p7SEBAICTmNPjDz7++GO98soreuGFF7y2jIyvnk8yZswYX+3o0aNVX18vSUpPT9e+fft8/UeOHNH+/fuVnp7u1ezdu9dXc3T962qO7T/adnRcR9fHjx/f6XmFQiGFQqEuzhwAAOAfnN6RKi0tVWpqqgoLC722nJwcZWZmqq6uzlf7l7/8RUOHDpUkRSIRNTU1qbq62ut/9dVXFYvFlJeX59W88cYbOnz4sFdTUVGhkSNHKiUlxauprKz0HaeiokKRSESSNGzYMKWnp/tqotGoNm3a5NUAAACcsO5+O72trc2ys7NtwYIF7fqWL19u4XDYysrKbOfOnbZw4UJLSEiwXbt2eTXTpk2zCRMm2KZNm+ytt96yc845x2688Uavv6mpydLS0uwnP/mJ1dbW2po1aywxMdGefPJJr+ZPf/qT9enTxx555BHbsWOHLVq0yPr27Wvbtm3zapYsWWLJycn24osv2tatW+2qq66yYcOGWUtLy3GfK3ftAQBw6unJ63e3g9SGDRtMktXV1XXYX1JSYmeddZYlJiZaJBKxN99809f/97//3W688UYbMGCAhcNhu/XWW+3AgQO+mvfee88uvvhiC4VCduaZZ9qSJUvaHef555+3c88914LBoJ133nn20ksv+fpjsZjdf//9lpaWZqFQyKZMmdLpmDtDkAIA4NTTk9fvgJlZr74ldhKLRqNKSkpSc3OzwuFwbw8HAAAch568fvNHiwEAABwRpAAAABwRpAAAABwRpAAAABw5PZATAODXFjNt3r1f+w60KvWMBOUOG6j4uEBvDwvAt4wgBQAnqLy2QYvXbVdDc6vXlpGUoEVXjNG0sRldbAngVMdHewBwAsprG/Sz1e/6QpQkNTa36mer31V5bUMvjQxATyBIAYCjtphp8brt6uhhfEfbFq/brrYYj+sDTlcEKQBwtHn3/nbvRB3LJDU0t2rz7v09NygAPYogBQCO9h3oPES51AE49RCkAMBR6hkJ32gdgFMPQQoAHOUOG6iMpAR19pCDgL66ey932MCeHBaAHkSQAgBH8XEBLbpijCS1C1NH1xddMYbnSQGnMYIUAJyAaWMz9PiPJyo9yf/xXXpSgh7/8USeIwWc5nggJwCcoGljM/SDMek82Rz4DiJIAcA3ID4uoMjwQb09DAA9jI/2AAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHBGkAAAAHHUrSOXk5CgQCLRbioqKJEmXXnppu745c+b49tHR9mvWrPHVvP7665o4caJCoZBGjBihVatWtRvLihUrlJOTo4SEBOXl5Wnz5s2+/tbWVhUVFWnQoEEaMGCArrvuOu3du7c7pwsAANClbgWpLVu2qKGhwVsqKiokSddff71Xc/vtt/tqli5d2m4/paWlvpqrr77a69u9e7cKCwt12WWXqaamRnfffbduu+02bdiwwat57rnnNG/ePC1atEjvvvuuzj//fBUUFGjfvn1ezT333KN169aprKxMf/zjH/Xpp5/q2muv7c7pAgAAdClgZua68d13363f//732rlzpwKBgC699FKNHz9ev/jFLzo/YCCg3/3ud77wdKwFCxbopZdeUm1trdf27//+72pqalJ5ebkkKS8vT//yL/+iX/3qV5KkWCymrKws3XnnnfrP//xPNTc3a8iQIXrmmWf0b//2b5KkDz74QKNHj1ZVVZUuvPDC4zq/aDSqpKQkNTc3KxwOH9c2AACgd/Xk9dv5O1KHDh3S6tWrNWPGDAUCAa/96aef1uDBgzV27FgVFxfryy+/bLdtUVGRBg8erNzcXD311FM6NstVVVUpPz/fV19QUKCqqirvuNXV1b6auLg45efnezXV1dU6fPiwr2bUqFHKzs72ajpy8OBBRaNR3wIAANCZPq4brl27Vk1NTbrlllu8th/96EcaOnSoMjMztXXrVi1YsEB1dXV64YUXvJqHHnpIl19+uRITE/WHP/xBd9xxhz7//HPdddddkqTGxkalpaX5jpWWlqZoNKqWlhZ99tlnamtr67Dmgw8+8PYRDAaVnJzcrqaxsbHTcyopKdHixYtdXg4AAPAd5BykVq5cqenTpyszM9NrmzVrlvfvcePGKSMjQ1OmTNGHH36o4cOHS5Luv/9+r2bChAn64osvtGzZMi9I9abi4mLNmzfPW49Go8rKyurFEQEAgJOZ00d7H3/8sV555RXddtttXdbl5eVJknbt2tVlzV//+lcdPHhQkpSent7u7rq9e/cqHA6rX79+Gjx4sOLj4zusSU9P9/Zx6NAhNTU1dVrTkVAopHA47FsAAAA64xSkSktLlZqaqsLCwi7rampqJEkZGRld1qSkpCgUCkmSIpGIKisrfTUVFRWKRCKSpGAwqEmTJvlqYrGYKisrvZpJkyapb9++vpq6ujrV19d7NQAAACeq2x/txWIxlZaW6qc//an69PnH5h9++KGeeeYZ/eu//qsGDRqkrVu36p577tHkyZP1ve99T5K0bt067d27VxdeeKESEhJUUVGhhx9+WP/xH//h7WfOnDn61a9+pfvuu08zZszQq6++queff14vvfSSVzNv3jz99Kc/1QUXXKDc3Fz94he/0BdffKFbb71VkpSUlKSZM2dq3rx5GjhwoMLhsO68805FIpHjvmMPAADga1k3bdiwwSRZXV2dr72+vt4mT55sAwcOtFAoZCNGjLD58+dbc3OzV/Pyyy/b+PHjbcCAAda/f387//zz7YknnrC2tjbfvl577TUbP368BYNBO/vss620tLTdOB599FHLzs62YDBoubm59vbbb/v6W1pa7I477rCUlBRLTEy0a665xhoaGrp1rs3NzSbJdw4AAODk1pPX7xN6jtTpjudIAQBw6jklniMFAADwXUeQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcESQAgAAcNStIJWTk6NAINBuKSoqkiRdeuml7frmzJnj20d9fb0KCwuVmJio1NRUzZ8/X0eOHPHVvP7665o4caJCoZBGjBihVatWtRvLihUrlJOTo4SEBOXl5Wnz5s2+/tbWVhUVFWnQoEEaMGCArrvuOu3du7c7pwsAANClbgWpLVu2qKGhwVsqKiokSddff71Xc/vtt/tqli5d6vW1tbWpsLBQhw4d0saNG/Wb3/xGq1at0gMPPODV7N69W4WFhbrssstUU1Oju+++W7fddps2bNjg1Tz33HOaN2+eFi1apHfffVfnn3++CgoKtG/fPq/mnnvu0bp161RWVqY//vGP+vTTT3Xttdd2/xUCAADojJ2AuXPn2vDhwy0Wi5mZ2SWXXGJz587ttH79+vUWFxdnjY2NXtvjjz9u4XDYDh48aGZm9913n5133nm+7X74wx9aQUGBt56bm2tFRUXeeltbm2VmZlpJSYmZmTU1NVnfvn2trKzMq9mxY4dJsqqqquM+v+bmZpNkzc3Nx70NAADoXT15/Xb+jtShQ4e0evVqzZgxQ4FAwGt/+umnNXjwYI0dO1bFxcX68ssvvb6qqiqNGzdOaWlpXltBQYGi0ajef/99ryY/P993rIKCAlVVVXnHra6u9tXExcUpPz/fq6murtbhw4d9NaNGjVJ2drZX05GDBw8qGo36FgAAgM70cd1w7dq1ampq0i233OK1/ehHP9LQoUOVmZmprVu3asGCBaqrq9MLL7wgSWpsbPSFKEneemNjY5c10WhULS0t+uyzz9TW1tZhzQcffODtIxgMKjk5uV3N0eN0pKSkRIsXLz7+FwEAAHynOQeplStXavr06crMzPTaZs2a5f173LhxysjI0JQpU/Thhx9q+PDhJzbSHlBcXKx58+Z569FoVFlZWb04IgAAcDJzClIff/yxXnnlFe+dps7k5eVJknbt2qXhw4crPT293d11R++kS09P9/77z3fX7d27V+FwWP369VN8fLzi4+M7rDl2H4cOHVJTU5PvXaljazoSCoUUCoW6PCcAAICjnL4jVVpaqtTUVBUWFnZZV1NTI0nKyMiQJEUiEW3bts13d11FRYXC4bDGjBnj1VRWVvr2U1FRoUgkIkkKBoOaNGmSryYWi6mystKrmTRpkvr27eurqaurU319vVcDAABwwrr77fS2tjbLzs62BQsW+Np37dplDz30kL3zzju2e/due/HFF+3ss8+2yZMnezVHjhyxsWPH2tSpU62mpsbKy8ttyJAhVlxc7NV89NFHlpiYaPPnz7cdO3bYihUrLD4+3srLy72aNWvWWCgUslWrVtn27dtt1qxZlpyc7LsbcM6cOZadnW2vvvqqvfPOOxaJRCwSiXTrXLlrDwCAU09PXr+7HaQ2bNhgkqyurs7XXl9fb5MnT7aBAwdaKBSyESNG2Pz589udxJ49e2z69OnWr18/Gzx4sN177712+PBhX81rr71m48ePt2AwaGeffbaVlpa2G8ejjz5q2dnZFgwGLTc3195++21ff0tLi91xxx2WkpJiiYmJds0111hDQ0O3zpUgBQDAqacnr98BM7NefUvsJBaNRpWUlKTm5maFw+HeHg4AADgOPXn95m/tAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOCJIAQAAOOrT2wP4LmqLmTbv3q99B1qVekaCcocNVHxcoLeHBQAAuokg1cPKaxu0eN12NTS3em0ZSQladMUYTRub0YsjAwAA3cVHez2ovLZBP1v9ri9ESVJjc6t+tvpdldc29NLIAACAC4JUD2mLmRav2y7roO9o2+J129UW66gCAACcjAhSPWTz7v3t3ok6lklqaG7V5t37e25QAADghBCkesi+A52HKJc6AADQ+whSPST1jIRvtA4AAPQ+glQPyR02UBlJCersIQcBfXX3Xu6wgT05LAAAcAIIUj0kPi6gRVeMkaR2Yero+qIrxvA8KQAATiEEqR40bWyGHv/xRKUn+T++S09K0OM/nshzpAAAOMXwQM4eNm1shn4wJp0nmwMAcBogSPWC+LiAIsMH9fYwAADACeKjPQAAAEcEKQAAAEcEKQAAAEcEKQAAAEcEKQAAAEcEKQAAAEcEKQAAAEcEKQAAAEcEKQAAAEc82bwLZiZJikajvTwSAABwvI5et49ex79NBKkuHDhwQJKUlZXVyyMBAADddeDAASUlJX2rxwhYT8S1U1QsFtOnn36qM844Q4EAf1S4p0SjUWVlZemTTz5ROBzu7eF8ZzEPJwfm4eTAPJwcjncezEwHDhxQZmam4uK+3W8x8Y5UF+Li4nTWWWf19jC+s8LhML+wTgLMw8mBeTg5MA8nh+OZh2/7naij+LI5AACAI4IUAACAI4IUTjqhUEiLFi1SKBTq7aF8pzEPJwfm4eTAPJwcTsZ54MvmAAAAjnhHCgAAwBFBCgAAwBFBCgAAwBFBCgAAwBFBCt2Wk5OjQCDQbikqKpIkXXrppe365syZ49tHfX29CgsLlZiYqNTUVM2fP19Hjhzx1bz++uuaOHGiQqGQRowYoVWrVrUby4oVK5STk6OEhATl5eVp8+bNvv7W1lYVFRVp0KBBGjBggK677jrt3bv3m31Besk3MQ8dbb9mzRpfDfPQta+bB0mqqqrS5Zdfrv79+yscDmvy5MlqaWnx+vfv36+bbrpJ4XBYycnJmjlzpj7//HPfcbZu3arvf//7SkhIUFZWlpYuXdpuLGVlZRo1apQSEhI0btw4rV+/3tdvZnrggQeUkZGhfv36KT8/Xzt37vyGX5He8U3MQ0f7WLJkie84zEPXupqHPXv2dNgXCARUVlbm7eOUuz4Y0E379u2zhoYGb6moqDBJ9tprr5mZ2SWXXGK33367r6a5udnb/siRIzZ27FjLz8+3P//5z7Z+/XobPHiwFRcXezUfffSRJSYm2rx582z79u326KOPWnx8vJWXl3s1a9assWAwaE899ZS9//77dvvtt1tycrLt3bvXq5kzZ45lZWVZZWWlvfPOO3bhhRfaRRdd9O2/SD3gROfBzEySlZaW+mpaWlq8fubh633dPGzcuNHC4bCVlJRYbW2tffDBB/bcc89Za2urt49p06bZ+eefb2+//ba9+eabNmLECLvxxhu9/ubmZktLS7ObbrrJamtr7dlnn7V+/frZk08+6dX86U9/svj4eFu6dKlt377dFi5caH379rVt27Z5NUuWLLGkpCRbu3atvffee3bllVfasGHDfHN+qvom5mHo0KH20EMP+fbz+eefe/3Mw9frah6OHDni62toaLDFixfbgAED7MCBA2Z2al4fCFI4YXPnzrXhw4dbLBYzs68u4HPnzu20fv369RYXF2eNjY1e2+OPP27hcNgOHjxoZmb33XefnXfeeb7tfvjDH1pBQYG3npuba0VFRd56W1ubZWZmWklJiZmZNTU1Wd++fa2srMyr2bFjh0myqqoq9xM+SXV3Hsy+ClK/+93vOu1nHrrvn+chLy/PFi5c2Gn99u3bTZJt2bLFa3v55ZctEAjY3/72NzMze+yxxywlJcX7+TAzW7BggY0cOdJbv+GGG6ywsNC377y8PJs9e7aZmcViMUtPT7dly5Z5/U1NTRYKhezZZ589gTM+OXV3Hsy+ClLLly/vtJ956L5/nod/Nn78eJsxY4a3fipeH/hoDyfk0KFDWr16tWbMmOH7w85PP/20Bg8erLFjx6q4uFhffvml11dVVaVx48YpLS3NaysoKFA0GtX777/v1eTn5/uOVVBQoKqqKu+41dXVvpq4uDjl5+d7NdXV1Tp8+LCvZtSoUcrOzvZqThcu83BUUVGRBg8erNzcXD311FOyYx4txzx0zz/Pw759+7Rp0yalpqbqoosuUlpami655BK99dZb3jZVVVVKTk7WBRdc4LXl5+crLi5OmzZt8momT56sYDDo1RQUFKiurk6fffaZV9PVXO3evVuNjY2+mqSkJOXl5TEPx1iyZIkGDRqkCRMmaNmyZb6PlJiH7uns99JR1dXVqqmp0cyZM722U/H6wB8txglZu3atmpqadMstt3htP/rRjzR06FBlZmZq69atWrBggerq6vTCCy9IkhobG30/JJK89cbGxi5rotGoWlpa9Nlnn6mtra3Dmg8++MDbRzAYVHJycruao8c5XbjMgyQ99NBDuvzyy5WYmKg//OEPuuOOO/T555/rrrvuksQ8dNc/z8NHH30kSXrwwQf1yCOPaPz48fqf//kfTZkyRbW1tTrnnHPU2Nio1NRU33769OmjgQMH+n4ehg0b5qs59mcmJSWl07k6dh/HbtdRzenCZR4k6a677tLEiRM1cOBAbdy4UcXFxWpoaNB//dd/SWIeuquj30vHWrlypUaPHq2LLrrIazsVrw8EKZyQlStXavr06crMzPTaZs2a5f173LhxysjI0JQpU/Thhx9q+PDhvTHM057rPNx///1ezYQJE/TFF19o2bJlXpBC9/zzPMRiMUnS7Nmzdeutt0r66nWurKzUU089pZKSkl4b6+nMdR7mzZvn7eN73/uegsGgZs+erZKSkpPqT5KcKjr6vXRUS0uLnnnmGd/voFMVH+3B2ccff6xXXnlFt912W5d1eXl5kqRdu3ZJktLT09vdGXF0PT09vcuacDisfv36afDgwYqPj++w5th9HDp0SE1NTZ3WnA5c56Gzmr/+9a86ePCgJOahOzqah4yMDEnSmDFjfLWjR49WfX29pK9en3379vn6jxw5ov3793/tz8PRvq5qju0/druOak4HrvPQkby8PB05ckR79uyRxDx0x9f9Xvrtb3+rL7/8UjfffLOv/VS8PhCk4Ky0tFSpqakqLCzssq6mpkbSP36ZRSIRbdu2zXfxqKioUDgc9n7RRSIRVVZW+vZTUVGhSCQiSQoGg5o0aZKvJhaLqbKy0quZNGmS+vbt66upq6tTfX29V3M6cJ2HzmpSUlK8//tmHo5fR/OQk5OjzMxM1dXV+Wr/8pe/aOjQoZK+eo2bmppUXV3t9b/66quKxWJe+I1EInrjjTd0+PBhr6aiokIjR45USkqKV9PVXA0bNkzp6em+mmg0qk2bNjEPnaipqVFcXJz30SvzcPy+7vfSypUrdeWVV2rIkCG+9lPy+tCtr6YD/19bW5tlZ2fbggULfO27du2yhx56yN555x3bvXu3vfjii3b22Wfb5MmTvZqjt7dOnTrVampqrLy83IYMGdLh7a3z58+3HTt22IoVKzq8vTUUCtmqVats+/btNmvWLEtOTvbd7TFnzhzLzs62V1991d555x2LRCIWiUS+xVemZ53IPPzv//6v/fd//7dt27bNdu7caY899pglJibaAw884NUwD8ens3kwM1u+fLmFw2ErKyuznTt32sKFCy0hIcF27drl1UybNs0mTJhgmzZtsrfeesvOOecc3+MPmpqaLC0tzX7yk59YbW2trVmzxhITE9vddt+nTx975JFHbMeOHbZo0aIOb7tPTk62F1980bZu3WpXXXXVaXPbvdmJzcPGjRtt+fLlVlNTYx9++KGtXr3ahgwZYjfffLO3D+bh+HQ1D2ZmO3futEAgYC+//HK7vlPx+kCQgpMNGzaYJKurq/O119fX2+TJk23gwIEWCoVsxIgRNn/+/HbPL9qzZ49Nnz7d+vXrZ4MHD7Z7773XDh8+7Kt57bXXbPz48RYMBu3ss8+20tLSduN49NFHLTs724LBoOXm5trbb7/t629pabE77rjDUlJSLDEx0a655hpraGj4Zl6Ek8CJzMPLL79s48ePtwEDBlj//v3t/PPPtyeeeMLa2tp8+2Ievl5n83BUSUmJnXXWWZaYmGiRSMTefPNNX//f//53u/HGG23AgAEWDoft1ltv9Z6rc9R7771nF198sYVCITvzzDNtyZIl7Y7z/PPP27nnnmvBYNDOO+88e+mll3z9sVjM7r//fktLS7NQKGRTpkzpdMynohOZh+rqasvLy7OkpCRLSEiw0aNH28MPP+x7zpQZ83A8vm4eiouLLSsrq93vmqNOtetDwOyYe50BAABw3PiOFAAAgCOCFAAAgCOCFAAAgCOCFAAAgCOCFAAAgCOCFAAAgCOCFAAAgCOCFAAAgCOCFAAAgCOCFAAAgCOCFAAAgCOCFAAAgKP/B+bx7vzHQqiLAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"re-squaring up these new-found underpopped for Missouri\")\n",
    "#SQUARING UP UNDERPOPPED\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "#underPoppedList = list()\n",
    "#minDistrictPop, maxDistrictPop = 0.99 * aDP, 1.01 * aDP\n",
    "#for t,pop in enumerate(HDvPop):\n",
    "#    if pop < minDistrictPop and t in popHDlist:\n",
    "#        underPoppedList.append(t)\n",
    "startTime = time.time()\n",
    "maxGap = 0.9 * np.median(unitPop)\n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) ) \n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)  #NOTE - HAVE NOT MOVED TO AVOIDEDENCLAVE HERE (YET)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "3e18456c-b669-4068-bd86-015e00d20f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now squaring down the 3  newfound overpopped Missouri HDs\n",
      "NOTE: to be safe, we are NOT using the avoidedIsland timesaver.  Copy from old code; e.g. NC\n",
      "Let's now square down the 3 overPopped HDs to within 882\n",
      "working on squaring down HD 2334 time is now 0\n",
      "We have addressed overpop in a total of 3 HDs\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Now squaring down the\",len(overPoppedList),\" newfound overpopped Missouri HDs\")\n",
    "print(\"NOTE: to be safe, we are NOT using the avoidedIsland timesaver.  Copy from old code; e.g. NC\")\n",
    "#HDunitList = [ampedHDlist[t].copy() for t in range(nHDs)]\n",
    "#HDvPop =     [ampedHDpop[t]         for t in range(nHDs)]\n",
    "#unitUse =    [ampedUnitUse[u]       for u in range(nUnits)] #saving in case I need to re-run\n",
    "HDnShedUnits = [0]*nHDs\n",
    "#overPoppedList = list()\n",
    "startTime = time.time()\n",
    "#for t,pop in enumerate(HDvPop):\n",
    "#    if pop > maxDistrictPop and t in popHDlist:\n",
    "#        overPoppedList.append(t)\n",
    "\n",
    "maxGap = 0.9 * np.median(unitPop)   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))   \n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    if ii%60 == 0:\n",
    "        print(\"working on squaring down HD\",t,\"time is now\",int(time.time()-startTime))\n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t],unitNbrs)\n",
    "    if not (unbroken and noEnclave):\n",
    "        print(\"SKIPPING shedding attempt on overpopped HD\",t,\"with pop\",HDvPop[t],\"because it was not legal to start\")\n",
    "    else:\n",
    "        #avgDist = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        excess = HDvPop[t] - aDP\n",
    "        origExcess = excess\n",
    "        HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "        barredSet = barredJettisonSet  #new 4/10/24 for MN, ban CCB jettisoning from ALL HD's, not just near ones\n",
    "        #barredSet = set(get2nbrs([starterU],unitNbrs)).union(barredJettisonSet)  #weaker specification - must be close to be barred\n",
    "\n",
    "        for u in set(HDunitList[t]).difference(barredSet):\n",
    "            isBoundary = False\n",
    "            for uu in unitNbrs[u]:\n",
    "                if uu not in HDunitList[t]:\n",
    "                    isBoundary = True\n",
    "                    break\n",
    "            if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "                HDboundaryList.append(u)\n",
    "                HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "        currList = HDunitList[t].copy()\n",
    "        shedList, stillGoing = list(), True\n",
    "        while excess > maxGap and len(HDboundaryList) > 0 and stillGoing:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "            while i < len(HDboundaryScore) and notYetPicked and stillGoing:        \n",
    "                listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "                unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "                tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "                unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "                if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't drop any more units without creating an enclave\n",
    "                print(\"can't drop any more units to HD\",t,\"without creating an enclave\")\n",
    "            else: #add this eligible unit\n",
    "                shedList.append(unitNoToShed)\n",
    "                excess -= unitPop[unitNoToShed]        \n",
    "                del currList[currList.index(unitNoToShed) ]\n",
    "                del HDboundaryList[listNo]\n",
    "                del HDboundaryScore[listNo]\n",
    "                for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                    if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and\n",
    "                                                                      u not in barredSet): \n",
    "                        HDboundaryList.append(u)\n",
    "                        HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "                for uu in HDboundaryList.copy():\n",
    "                    if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                        del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                        del HDboundaryList[HDboundaryList.index(uu)]   \n",
    "        for u in shedList:\n",
    "            unitUse[u] -= HDweight[t] * nDistricts\n",
    "        HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "                 np.sum([unitPop[u] for u in shedList]) )\n",
    "\n",
    "        HDnShedUnits[t] = -1 * len(shedList)\n",
    "\n",
    "print(\"We have addressed overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "c8868263-191e-4ee7-9115-c2a247bedfc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.0007 1.00045 and their SDs are 0.10046 0.03915\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 4604\n",
      "uh-oh, HD 165 has contiguity, complement-contiguity of False True\n",
      "uh-oh, HD 180 has contiguity, complement-contiguity of False True\n",
      "working on HD 300 out of 4604\n",
      "working on HD 600 out of 4604\n",
      "working on HD 900 out of 4604\n",
      "working on HD 1200 out of 4604\n",
      "working on HD 1500 out of 4604\n",
      "working on HD 1800 out of 4604\n",
      "working on HD 2100 out of 4604\n",
      "working on HD 2400 out of 4604\n",
      "working on HD 2700 out of 4604\n",
      "working on HD 3000 out of 4604\n",
      "working on HD 3300 out of 4604\n",
      "working on HD 3600 out of 4604\n",
      "working on HD 3900 out of 4604\n",
      "working on HD 4200 out of 4604\n",
      "working on HD 4500 out of 4604\n",
      "all done checking HD and complement contiguity for all 4604 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "1d8c4974-b931-4c81-a759-ed102a415e54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t,fullpop, contig pop/aDP 165 1.00062 0.99849\n",
      "t,fullpop, contig pop/aDP 180 0.99886 0.979\n",
      "     above was discontig. No more enclavy\n",
      "we have 2 discontiguous HDs\n",
      "fixing where we can, the we will re-run squaring up / down where needed\n",
      "t,orig pop/aDP, contig pop/aDP 165 0.99849 0.99849\n",
      "t,orig pop/aDP, contig pop/aDP 180 0.979 0.979\n",
      "   we will shore up HD 180 right now\n",
      "re-squaring up these new-found underpopped for Missouri\n",
      "We will now square up the 1 HDs with pop < 761670 to within 882\n",
      "working on squaring up HD 180 time is now 0\n",
      "We have addressed underpop in a total of 1 HDs\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "contigTrouble = [165,180]\n",
    "#MO special triage\n",
    "for t in contigTrouble:\n",
    "    contigUs = getContigFromStarter(HDunitList[t][0],HDunitList[t], unitNbrs )\n",
    "    contigPop = np.sum([unitPop[u] for u in contigUs])\n",
    "    print(\"t,fullpop, contig pop/aDP\",t,r5(HDvPop[t]/aDP), r5(contigPop/aDP) )\n",
    "print(\"     above was discontig. No more enclavy\")\n",
    "\n",
    "print(\"we have\",len(contigTrouble),\"discontiguous HDs\")\n",
    "print(\"fixing where we can, the we will re-run squaring up / down where needed\")\n",
    "underPoppedList, overPoppedList = list(), list()\n",
    "minDistrictPop, maxDistrictPop = 0.99 * aDP, 1.01 * aDP\n",
    "for t in contigTrouble:\n",
    "    contigUs = getContigFromStarter(HDunitList[t][0],HDunitList[t], unitNbrs )\n",
    "    contigPop = np.sum([unitPop[u] for u in contigUs])\n",
    "    HDvPop[t] = contigPop\n",
    "    for u in set(HDunitList[t]).difference(set(contigUs)):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts\n",
    "    HDunitList[t] = contigUs.copy()\n",
    "    print(\"t,orig pop/aDP, contig pop/aDP\",t,r5(HDvPop[t]/aDP), r5(contigPop/aDP) )\n",
    "    if HDvPop[t] < minDistrictPop:\n",
    "        underPoppedList.append(t)\n",
    "        print(\"   we will shore up HD\",t,\"right now\")\n",
    "\n",
    "print(\"re-squaring up these new-found underpopped for Missouri\")\n",
    "#SQUARING UP UNDERPOPPED\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "#underPoppedList = list()\n",
    "#minDistrictPop, maxDistrictPop = 0.99 * aDP, 1.01 * aDP\n",
    "#for t,pop in enumerate(HDvPop):\n",
    "#    if pop < minDistrictPop and t in popHDlist:\n",
    "#        underPoppedList.append(t)\n",
    "startTime = time.time()\n",
    "maxGap = 0.9 * np.median(unitPop)\n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) ) \n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)  #NOTE - HAVE NOT MOVED TO AVOIDEDENCLAVE HERE (YET)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "8cb13f7e-fcff-4a99-bdc3-7208b2a07732",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.0007 1.00045 and their SDs are 0.10046 0.03915\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 4604\n",
      "working on HD 300 out of 4604\n",
      "working on HD 600 out of 4604\n",
      "working on HD 900 out of 4604\n",
      "working on HD 1200 out of 4604\n",
      "working on HD 1500 out of 4604\n",
      "working on HD 1800 out of 4604\n",
      "working on HD 2100 out of 4604\n",
      "working on HD 2400 out of 4604\n",
      "working on HD 2700 out of 4604\n",
      "working on HD 3000 out of 4604\n",
      "working on HD 3300 out of 4604\n",
      "working on HD 3600 out of 4604\n",
      "working on HD 3900 out of 4604\n",
      "working on HD 4200 out of 4604\n",
      "working on HD 4500 out of 4604\n",
      "all done checking HD and complement contiguity for all 4604 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "12a9401d-95a4-48a8-b7df-4baff80266e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file, finally fixed\n"
     ]
    }
   ],
   "source": [
    "print(\"Lets write these UNIT lists to a file, finally fixed\")   #DONE\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"fullyPatched.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4797c12b-9e9b-48b2-9cb5-541c7ffb2e42",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "26b87c95-101f-4f8f-bb73-feb1827ac6b5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    }
   ],
   "source": [
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))\n",
    "if noFrag == 1:\n",
    "    nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "ec102553-e4f3-4010-86bc-725a01b95788",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([v for v in range(len(parentVTDno))],parentVTDno)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "55d64c64-b473-49d0-b863-d4123cbb9b6c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confirming vtd 0 is not in the map but missing from the parent vtd list\n",
      "confirming vtd 2000 is not in the map but missing from the parent vtd list\n",
      "confirming vtd 4000 is not in the map but missing from the parent vtd list\n"
     ]
    }
   ],
   "source": [
    "for v in range(nVTDs):\n",
    "    if v %2000 == 0:\n",
    "        print(\"confirming vtd\",v,\"is not in the map but missing from the parent vtd list\")\n",
    "    if MAP.contains(vtdGeom[v].centroid) and v not in parentVTDno:\n",
    "        print(\"Uhoh,\",v,\"is in the map but not in the parent vtd list\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "ecd50c96-51ce-4d83-b0df-e8c645c35815",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after above patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on full or partial VTD master list for unit 0\n",
      "working on full or partial VTD master list for unit 2000\n",
      "working on full or partial VTD master list for unit 4000\n",
      "Now converting unit lists to vtd lists for all HDs\n"
     ]
    }
   ],
   "source": [
    "#THIS WORKS FOR BOTH VTD- AND UNIT-BASED (FRAG VTD) -- **NOTE: DOES NOT ADD IN CUT DISTRICTS AND REBALANCE WEIGHTS\n",
    "print(\"after above patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))  #for simple states where units are at least whole vtd's\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist, unitFragVTDlist, unitVTDfrac = [list() for u in range(nUnits)], [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "unitFullVTDlist, unitPartialVTDlist =       [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "\n",
    "for u in range(nUnits):\n",
    "    if u %2000 == 0:\n",
    "        print(\"working on full or partial VTD master list for unit\",u)\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "        unitFullVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "            unitFullVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        if noFrag == 1:\n",
    "            unitVTDlist[u].append(allUnits[u] )\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitVTDlist[u].append(surroundedVTDs[i])\n",
    "        else:\n",
    "            unitFragVTDlist[u].append(allUnits[u])\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitFragVTDlist[u].append(surroundedVTDs[i])\n",
    "            vtdFrac = [0. for V in range(nVTDs)]\n",
    "            for vv in unitFragVTDlist[u]:\n",
    "                V = parentVTDno[vv]\n",
    "                if len(VTDchildren[V]) == 1:  #nonfragmented vtd\n",
    "                    vtdFrac[V] = 1.\n",
    "                else:\n",
    "                    if tractPop[V] > 0:\n",
    "                        vtdFrac[V] += fragVTDgeom[vv].area / vtdGeom[V].area #fragVTDpop[uu] / tractPop[v]  #we overwrote the frag pops earlier; can't use\n",
    "            for v in range(nVTDs):\n",
    "                if vtdFrac[v] > 0.0001:\n",
    "                    if vtdFrac[v] > 0.999:\n",
    "                        unitFullVTDlist[u].append(v)\n",
    "                    else:\n",
    "                        unitPartialVTDlist[u].append(v)\n",
    "                        unitVTDfrac[u].append(vtdFrac[v] )\n",
    "                                    \n",
    "HDpartialVTDlist, HDfullVTDlist, HDvtdFrac = [list() for t in range(nHDs)] ,[list() for t in range(nHDs)],[list() for t in range(nHDs)]               \n",
    "print(\"Now converting unit lists to vtd lists for all HDs\")\n",
    "if noFrag == 1:\n",
    "    for t in popHDlist:\n",
    "        for u in HDunitList[t]:\n",
    "            vtdList[t] += unitVTDlist[u]\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "else:\n",
    "    for t in popHDlist:\n",
    "        partialList, partialFrac = list(), list()  #for many HDs, we'll recover whole VTDs when aggregating units\n",
    "        for u in HDunitList[t]:\n",
    "            HDfullVTDlist[t] +=   unitFullVTDlist[u] \n",
    "            partialList +=         unitPartialVTDlist[u]\n",
    "            partialFrac +=          unitVTDfrac[u]\n",
    "        partialSet = set(partialList)  #non-duplicated set of partials across the HD, prepping to aggregate where possible\n",
    "        partialSetFrac, partialSetList = [0. for v in partialSet], list(partialSet)\n",
    "        for i,v in enumerate(partialList):\n",
    "            partialSetFrac[partialSetList.index(v)] += partialFrac[i]\n",
    "        for i,v in enumerate(partialSetList):\n",
    "            if partialSetFrac[i] > 0.999:  #the district has all the fragments of the original VTD contained in the HD's units\n",
    "                HDfullVTDlist[t].append(v)\n",
    "            else:\n",
    "                HDpartialVTDlist[t].append(v)\n",
    "                HDvtdFrac[t].append(      r5(partialSetFrac[i]) )  #save the aggregated fraction of this VTD across all units\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "5075814f-4a75-4807-ad8d-b3cc969cb470",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4604 4604 4604 4604 4604 4604\n"
     ]
    }
   ],
   "source": [
    "\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDvPop = HDvPop[0:nHDs]\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDfullVTDlist = HDfullVTDlist[0:nHDs]\n",
    "HDpartialVTDlist = HDpartialVTDlist[0:nHDs]\n",
    "HDvtdFrac = HDvtdFrac[0:nHDs]\n",
    "hdCPx, hdCPy = hdCPx[0:nHDs], hdCPy[0:nHDs]\n",
    "print(nHDs,len(tList), len(HDweight), len(HDvPop), len(HDvtdFrac), len(hdCPx) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "e385f357-5b7e-46f5-b054-c5c38d799395",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999999998 4604\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight), nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "8d922813-2854-41d7-9405-22db5d5d5a59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now append the cut districts and adjust the HDweights\n",
      "8 0 are the number of HD-drawn and cut districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Now append the cut districts and adjust the HDweights\")\n",
    "print(nDistricts,nCutDistricts,\"are the number of HD-drawn and cut districts\")\n",
    "HDweight = [nDistricts/float(nCutDistricts + nDistricts) * HDweight[t] for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "if type(hdCPx) != type(list()):\n",
    "    hdCPx, hdCPy = hdCPx.to_list(), hdCPy.to_list()\n",
    "for L in allCutLists:\n",
    "    tList.append(len(tList))\n",
    "    HDweight.append(1./(nCutDistricts + nDistricts))\n",
    "    pop = np.sum([tractPop[v] for v in L])\n",
    "    HDvPop.append(pop )\n",
    "    HDfullVTDlist.append(L)\n",
    "    HDpartialVTDlist.append(list() )\n",
    "    HDvtdFrac.append(list() )\n",
    "    hdCPx.append(np.sum([tractPop[v]*tractCPx[v] for v in L]) / pop )\n",
    "    hdCPy.append(np.sum([tractPop[v]*tractCPy[v] for v in L]) / pop )\n",
    "    \n",
    "\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                        \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedWithCutsVTDs.csv\"   #patchDown, PatchUp, PatchBoth #patchedWithCutsVTDs #patchedVTDs\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "b0b611f7-6c35-495d-b38c-c848317d63ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999999998\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "b9d808de-119f-4197-8e0c-8407c105d789",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking that VTDs are represented 1.0 across all units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"checking that VTDs are represented 1.0 across all units\")\n",
    "vtdUSE = [0. for v in range(nVTDs)]\n",
    "for u in range(nUnits):\n",
    "    for v in unitFullVTDlist[u]:\n",
    "        vtdUSE[v] += 1\n",
    "    for i,v in enumerate(unitPartialVTDlist[u]):\n",
    "        vtdUSE[v] += unitVTDfrac[u][i]\n",
    "plotVTDs, plotUses = list(), list()\n",
    "for v in range(nVTDs):\n",
    "    if tractPop[v] > 0  and v not in allCutVTDs:\n",
    "        plotVTDs.append(v)\n",
    "        plotUses.append(vtdUSE[v])\n",
    "plt.scatter(plotVTDs, plotUses)\n",
    "plt.show()\n",
    "for i,v in enumerate(plotVTDs):\n",
    "    if plotUses[i] > 0.1 and plotUses[i] < 0.9 :\n",
    "        print(v,tractPop[v], plotUses[i],\"vtd no, its pop, and usage across all units\")\n",
    "        plotPoly(tractGeom[v])\n",
    "unused = list()\n",
    "for v in range(nVTDs):\n",
    "    if vtdUSE[v] < 0.2 and MAP.contains(vtdGeom[v].centroid) and tractPop[v] > 0:\n",
    "        unused.append(v)\n",
    "        #print(v,\"in county\",countyNo[v],\"has pop\",tractPop[v],\"and map-total usage of\",vtdUSE[v],\"its surroundedness is\",v in surroundedVTDs)\n",
    "        plotPoly(vtdGeom[v].centroid.buffer(0.1))\n",
    "        plotCenter(v,vtdGeom[v],8)\n",
    "    #if vtdUSE[v] > 0.2 and vtdUSE[v] < 0.95:\n",
    "    #    plotCenter(r3(vtdUSE[v]),vtdGeom[v])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "3d2a393c-5ab8-4140-80eb-1153c2ef41f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if unitUse[u]  < 0.9:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "    if  unitUse[u]  > 1.1:\n",
    "        plotPoly(unitGeom[u],1.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "404935e6-f0de-4b51-9db9-020aa208f852",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integrity check: vtd-based pops.  These should all be within single digits\n",
      "x = n surrounded, y= unit - vtd-based\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t, HDunit-based pop - pop from vtd-based HDs 1503 -11.298\n",
      "t, HDunit-based pop - pop from vtd-based HDs 2599 -11.009\n",
      "Here are the HD centers with significant pop difference between unit-based and vtd-based HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"integrity check: vtd-based pops.  These should all be within single digits\")\n",
    "HDvtdbasedPop = [0. for t in range(nHDs)]\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "nSurrs = [0. for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "        if u in surroundedVTDs:\n",
    "            nSurrs[t] += 1\n",
    "    for v in HDfullVTDlist[t]:\n",
    "        HDvtdbasedPop[t] += origVTDpop[v] #tractPop[v]\n",
    "    for i,v in enumerate(HDpartialVTDlist[t]):\n",
    "        HDvtdbasedPop[t] += HDvtdFrac[t][i] * origVTDpop[v] #tractPop[v] also works\n",
    "        \n",
    "plt.scatter([nSurrs[t] for t in popHDlist], [HDvPop[t] - HDvtdbasedPop[t] for t in popHDlist] )\n",
    "print(\"x = n surrounded, y= unit - vtd-based\")\n",
    "plt.show()\n",
    "plotThis = False\n",
    "for t in popHDlist:  #seems like for TN, we lost a vtd in converting from unit-based to vtd-based\n",
    "    if abs(HDvPop[t] - HDvtdbasedPop[t]) > 10:\n",
    "        plotThis = True\n",
    "        print(\"t, HDunit-based pop - pop from vtd-based HDs\",t,r3(HDvPop[t] - HDvtdbasedPop[t]) )\n",
    "        plotPoly(tractCP[t].buffer(0.1))\n",
    "if plotThis:\n",
    "    plotPoly(MAP)\n",
    "    print(\"Here are the HD centers with significant pop difference between unit-based and vtd-based HD lists\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef27af54-daf5-4e8f-9230-bce5b0b1e705",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "3ad3342b-b8e3-46f5-a3e5-f49df386b88f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\n",
      "Let's read in the 2020 and 2016 voting data for MO\n",
      "In year/election 2020 Dem and GOP total votes were 1253000 1718714 for a stateGOP of 0.57836\n",
      "There are a total of 4604  vote-mapped voting districts from election(s) in year  2020\n",
      "In year/election 2016 Dem and GOP total votes were 1071064 1594495 for a stateGOP of 0.59818\n",
      "There are a total of 4604  vote-mapped voting districts from election(s) in year  2016\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\")\n",
    "#note: we started w possibility of separate election --> 2020 vtd files by year.  Now we use ALARM combined 2016+2020 --> 2020 csv's for all exc CA\n",
    "# copied from \"HDpartisanAnalysis\"\n",
    "print(\"Let's read in the 2020 and 2016 voting data for\",STATE)\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G16PREDCLI\"], [\"G16PRERTRU\" ], list()\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G20PREDBID\", \"G16PREDCli\"], [\"G20PRERTRU\" ,\"G16PRERTru\" ], list()\n",
    "DemCandidates, RepCandidates, vestDFs = [\"pre_20_dem_bid\", \"pre_16_dem_cli\"], [\"pre_20_rep_tru\" ,\"pre_16_rep_tru\" ], list()\n",
    "nYears = 2\n",
    "unitIDs, vestReps,vestDems,yearLean = [list()]*nYears, [0]*nYears, [0]*nYears, [0.5]*nYears\n",
    "years = [str(2020),str(2016)]\n",
    "DemVotes, RepVotes = [list() for y in years], [list() for y in years]\n",
    "vestDir = \"./state_map_files/\" + STATE.lower()\n",
    "vestDF = pd.read_csv(vestDir + \"_2020_vtd.csv\")\n",
    "mergedDF = vestDF.copy() #pd.merge(mergedDF,vestDF, on = \"GEOID20\")\n",
    "for y,year in enumerate(years):\n",
    "    DemVotes[y], RepVotes[y], unitIDs[y] = mergedDF[DemCandidates[y]], mergedDF[RepCandidates[y]], vestDF[(\"GEOID20\")]\n",
    "    vestDems[y], vestReps[y] = np.sum(DemVotes[y]), np.sum(RepVotes[y])\n",
    "    yearLean[y] = vestReps[y]/float(vestDems[y]+vestReps[y])\n",
    "    print(\"In year/election\",year,\"Dem and GOP total votes were\",int(vestDems[y]),int(vestReps[y]),\"for a stateGOP of\",r5(yearLean[y]) )\n",
    "    nVTDs = len(DemVotes[y])\n",
    "    print(\"There are a total of\",nVTDs,\" vote-mapped voting districts from election(s) in year \",years[y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f628069d-4177-401e-bf49-b7f194d0ef8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "5cf73783-4a48-4a3f-a2e3-c1b9ff32c859",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's read in our ensemble of HD districts for MO\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the filename with the vtd assignments to districts; e.g. NC2666patchedWithCutsVTDs.csv MO4604patchedVTDs.csv\n",
      "enter the number of Congr'l districts in the state; e.g. 8 8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This ensemble or enacted map describes 4604 drawn districts.  This state has 8 districts per map\n",
      "converting vtd list strings to vtd lists by drawn district ...\n",
      "the total weight across all rows should be 1.000, actually is 1.0\n",
      "I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\n",
      "translating from HD order of precinct rows to VEST order\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's read in our ensemble of HD districts for\",STATE)\n",
    "infilename = input(\"enter the filename with the vtd assignments to districts; e.g. NC2666patchedWithCutsVTDs.csv\")\n",
    "\n",
    "HDdf = pd.read_csv(\"2024state_HD_output/\"+infilename)\n",
    "nRows = len(HDdf)\n",
    "nStateDistricts = int(input(\"enter the number of Congr'l districts in the state; e.g. 8\"))\n",
    "print(\"This ensemble or enacted map describes\",nRows,\"drawn districts.  This state has\",nStateDistricts,\"districts per map\")\n",
    "\n",
    "HDvPop = HDdf[\"HDvPop\"].to_list()\n",
    "HDweight = HDdf['HDweight'].to_list()\n",
    "#tractPop = HDdf['tractPop'].to_list()\n",
    "#statePop = np.sum(tractPop)\n",
    "tractCPx = HDdf['centroid x'].to_list()\n",
    "tractCPy = HDdf['centroid y'].to_list()\n",
    "HDvtdListString = HDdf[\"HDvtdList\"]\n",
    "print(\"converting vtd list strings to vtd lists by drawn district ...\")\n",
    "HDvtdList = [list() for t in range(nRows) ]\n",
    "for t in range(nRows):\n",
    "    if HDvtdListString[t] != \"[]\":\n",
    "        HDvtdList[t] = ast.literal_eval(HDvtdListString[t])\n",
    "\n",
    "if \"partials\" in HDdf.columns.values: #\"splitTractNo\" #.to_list():\n",
    "    splitTractList, splitTractUseList = HDdf['partials'], HDdf['HDvtdFrac']\n",
    "    splitTractNo = [ast.literal_eval(splitTractList[t])    for t in range(nRows)]\n",
    "    splitTractUse = [ast.literal_eval(splitTractUseList[t]) for t in range(nRows)]\n",
    "else :  #incoming file didn't use any partial units, only whole ones\n",
    "    splitTractNo, splitTractUse = [[] for t in range(nRows)] , [ [] for t in range(nRows)]\n",
    "\n",
    "print(\"the total weight across all rows should be 1.000, actually is\",r5(np.sum(HDweight)) )\n",
    "print(\"I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\")\n",
    "censusGEOID20 = [ str( tractPopFile['GEOID20'][i]) for i in range(len(tractPopFile['GEOID20'])) ] #force strings to match\n",
    "miniHDdf = pd.DataFrame( {\"GEOID20\":censusGEOID20} )\n",
    "vestNo = [v for v in range(nVTDs)]\n",
    "print(\"translating from HD order of precinct rows to VEST order\")\n",
    "mergedGEO = [str(mergedDF['GEOID20'][i]) for i in range(len(mergedDF['GEOID20'])) ]  #force strings to match\n",
    "miniVestDF = pd.DataFrame( {\"GEOID20\":mergedGEO, \"vestNo\":vestNo} )\n",
    "mappingDF = pd.merge(miniHDdf,miniVestDF,on=\"GEOID20\")\n",
    "vestID = mappingDF['vestNo']  #this maps each named unit in a HDVL to the correct vestNo row with voting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "540c6d1d-7728-48bf-90f0-f124b25ac7c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sanity check - Dem-leaning vtds for year 2020\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"sanity check - Dem-leaning vtds for year\",years[0])  #for MA, do GOP-leaning.  Other states, use Dem-leaning\n",
    "for v in range(nVTDs):\n",
    "    plotPoly(vtdGeom[v],0.2)\n",
    "    if DemVotes[0][vestID[v]] > RepVotes[0][vestID[v]]:\n",
    "        plt.text(vtdGeom[v].centroid.x, vtdGeom[v].centroid.y, \"D\", color = \"blue\",fontsize=6)\n",
    "        #plotCenter(\"d\",vtdGeom[v],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "56ed7e9d-1400-4848-892e-737555552d58",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\n",
      "working on drawn district no 0\n",
      "working on drawn district no 1000\n",
      "working on drawn district no 2000\n",
      "working on drawn district no 3000\n",
      "working on drawn district no 4000\n",
      "here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\n",
      "true 2020: 1253000 1718714 465713\n",
      "ensemble : 1254309 1721121 466811\n",
      "the true and ensemble state GOP leans are 0.57836 0.5784443862288964\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\")\n",
    "nDrawnDistricts = len(HDweight)  #now including cut districts\n",
    "nMapDistricts = nCutDistricts + nDistricts\n",
    "nEnsembleDems, nEnsembleReps = [0.]*nYears, [0.]*nYears\n",
    "y=0\n",
    "for t in range(nDrawnDistricts):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on drawn district no\",t)\n",
    "    nEnsembleDems[y] += nMapDistricts* HDweight[t] * ( np.sum([DemVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([DemVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "    nEnsembleReps[y] += nMapDistricts* HDweight[t] * ( np.sum([RepVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([RepVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "\n",
    "print(\"here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\")\n",
    "print(\"true 2020:\",int(vestDems[y]),int(vestReps[y]),              int(vestReps[y] - vestDems[y]) )\n",
    "print(\"ensemble :\",int(nEnsembleDems[y]),int(nEnsembleReps[y]),    int(nEnsembleReps[y] - nEnsembleDems[y]) )\n",
    "print(\"the true and ensemble state GOP leans are\",r5(vestReps[y]/(vestReps[y]+vestDems[y])),\n",
    "      nEnsembleReps[y]/(nEnsembleReps[y]+nEnsembleDems[y]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "5c607c63-df0d-4334-8131-368c090c76d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total drawn districts, HD-drawn, map districts 4604 4604 8\n"
     ]
    }
   ],
   "source": [
    "print(\"total drawn districts, HD-drawn, map districts\",nDrawnDistricts,nHDs, nMapDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb0d0d12-6bf4-4d5f-9236-fa8735393e98",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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